{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polynomial Regression\n",
    "\n",
    "This example has been contributed by Tyler James Burch ([\\@tjburch](https://github.com/tjburch) on GitHub)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import arviz as az\n",
    "import bambi as bmb\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "SEED = 1234\n",
    "az.style.use(\"arviz-darkgrid\")\n",
    "warnings.filterwarnings(\"ignore\") # Temporary fix to make outputs cleaner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example will discuss polynomial regression using Bambi. Unlike many other examples shown, there aren't specific polynomial methods or families implemented in Bambi, most of the interesting behavior for polynomial regression occurs within the formula definition. Regardless, there are some nuances that are useful to be aware of. \n",
    "\n",
    "This example uses the kinematic equations from classical mechanics as a backdrop. Specifically, an object in motion experiencing constant acceleration can be described by the following:\n",
    "\n",
    "$$x_f = \\frac{1}{2} a t^2 + v_0 t + x_0$$\n",
    "\n",
    "where $x_0$ and $x_f$ are the initial and final locations, $v_0$ is the initial velocity, and $a$ is acceleration.\n",
    "\n",
    "## A falling ball\n",
    "\n",
    "First, we'll consider a simple falling ball, released from 50 meters. In this situation, $v_0 = 0$ $m$/$s$, $x_0 = 50$ $m$ and $a = g$, the acceleration due to gravity, $-9.81$ $m$/$s^2$. So dropping out the $v_0 t$ component, the equation takes the form:\n",
    "\n",
    "$$x_f = \\frac{1}{2} g t^2 + x_0$$\n",
    "\n",
    "We'll start by simulating data for the first 2 seconds of motion. We will also assume some measurement error with a gaussian distribution of $\\sigma = 0.3$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = -9.81  # acceleration due to gravity (m/s^2)\n",
    "t = np.linspace(0, 2, 100)  # time in seconds\n",
    "inital_height = 50\n",
    "x_falling = 0.5 * g * t**2 + inital_height\n",
    "\n",
    "rng = np.random.default_rng(SEED)\n",
    "noise = rng.normal(0, 0.3, x_falling.shape)\n",
    "x_obs_falling = x_falling + noise\n",
    "df_falling = pd.DataFrame({\"t\": t, \"x\": x_obs_falling})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.scatter(t, x_obs_falling, label=\"Observed Displacement\", color=\"C0\")\n",
    "ax.plot(t, x_falling, label=\"True Function\", color=\"C1\")\n",
    "ax.set(xlabel=\"Time (s)\", ylabel=\"Displacement (m)\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Casting the equation $x_f = \\frac{1}{2} g t^2 + x_0$ into a regression context, we let time ($t$) be the independent variable, and final location ($x_f$) be the response/dependent variable. This allows our coefficients to be proportional to $g$ and $x_0$. The intercept, $\\beta_0$ corresponds exactly to $x_0$. Letting $\\beta_1 = \\frac{1}{2} g$ then gives $g = 2\\beta_1$ when $x_1 = t^2$, meaning we're doing _polynomial regression_. We can put this into Bambi via the following, optionally including the `+ 1` to emphasize that we choose to include the coefficient. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, Intercept, I(t ** 2)]\n"
     ]
    },
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      "text/plain": [
       "Output()"
      ]
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n"
     ]
    }
   ],
   "source": [
    "model_falling = bmb.Model(\"x ~ I(t**2) + 1\", df_falling)\n",
    "results_falling = model_falling.fit(idata_kwargs={\"log_likelihood\": True}, random_seed=SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The term `I(t**2)` indicates to evaluate inside the `I`. For including _just the $t^2$ term_, you can express it in any of the following ways:  \n",
    "\n",
    "- `I(t**2)`\n",
    "- `{t**2}`\n",
    "- Square the data directly, and pass it as a new column\n",
    "\n",
    "To verify, we'll fit the other two versions as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, Intercept, I(t ** 2)]\n"
     ]
    },
    {
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       "model_id": "f71d4dbed75340f0ad2419fcd4521d94",
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      },
      "text/plain": [
       "Output()"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, Intercept, tsquared]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47fd5e464b184957b5c8cf10c6bbf108",
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I{t**2} coefficient:  -4.8476\n",
      "{t**2} coefficient:  -4.8476\n",
      "tsquared coefficient:  -4.8476\n"
     ]
    }
   ],
   "source": [
    "model_falling_variation1 = bmb.Model(\n",
    "    \"x ~ {t**2} + 1\",  # Using {t**2} syntax\n",
    "    df_falling\n",
    ")\n",
    "results_variation1 = model_falling_variation1.fit(random_seed=SEED)\n",
    "\n",
    "model_falling_variation2 = bmb.Model(\n",
    "    \"x ~ tsquared + 1\",  # Using data with the t variable squared\n",
    "    df_falling.assign(tsquared=t**2)  # Creating the tsquared variable for use in the formula\n",
    ")\n",
    "results_variation2 = model_falling_variation2.fit(random_seed=SEED)\n",
    "\n",
    "print(\"I{t**2} coefficient: \", round(results_falling.posterior[\"I(t ** 2)\"].values.mean(), 4))\n",
    "print(\"{t**2} coefficient: \", round(results_variation1.posterior[\"I(t ** 2)\"].values.mean(), 4))\n",
    "print(\"tsquared coefficient: \", round(results_variation2.posterior[\"tsquared\"].values.mean(), 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each of these provides identical results, giving -4.9, which is $g/2$. This makes the acceleration exactly the $-9.81$ $m$/$s^2$ acceleration that generated the data. Looking at our model summary,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.336</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.289</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5977.0</td>\n",
       "      <td>2861.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>49.961</td>\n",
       "      <td>0.051</td>\n",
       "      <td>49.870</td>\n",
       "      <td>50.058</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>5997.0</td>\n",
       "      <td>3145.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(t ** 2)</th>\n",
       "      <td>-4.848</td>\n",
       "      <td>0.028</td>\n",
       "      <td>-4.899</td>\n",
       "      <td>-4.799</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5704.0</td>\n",
       "      <td>2844.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "sigma       0.336  0.025   0.289    0.381      0.000    0.000    5977.0   \n",
       "Intercept  49.961  0.051  49.870   50.058      0.001    0.001    5997.0   \n",
       "I(t ** 2)  -4.848  0.028  -4.899   -4.799      0.000    0.000    5704.0   \n",
       "\n",
       "           ess_tail  r_hat  \n",
       "sigma        2861.0    1.0  \n",
       "Intercept    3145.0    1.0  \n",
       "I(t ** 2)    2844.0    1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(results_falling)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that both $g/2 = -4.9$ (so $g=-9.81$) and the original height of $x_0 = 50$ $m$ are recovered, along with the injected noise.\n",
    "\n",
    "We can then use the model to answer some questions, for example, when would the ball land? This would correspond to $x_f = 0$.\n",
    "\n",
    "$$\n",
    "0 = \\frac{1}{2} g t^2 - x_0\n",
    "$$\n",
    "\n",
    "$$\n",
    "t = \\sqrt{2x_0 / g}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ball will land at 3.21 seconds\n"
     ]
    }
   ],
   "source": [
    "calculated_x0 = results_falling.posterior[\"Intercept\"].values.mean()\n",
    "calculated_g = -2 * results_falling.posterior[\"I(t ** 2)\"].values.mean()\n",
    "calculated_land = np.sqrt(2 * calculated_x0 / calculated_g)\n",
    "print(f\"The ball will land at {round(calculated_land, 2)} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or if we want to account for our measurement error and use the full posterior,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ball landing will be measured between 3.2 and 3.23 seconds\n"
     ]
    }
   ],
   "source": [
    "calculated_x0_posterior = results_falling.posterior[\"Intercept\"].values\n",
    "calculated_g_posterior = -2 * results_falling.posterior[\"I(t ** 2)\"].values\n",
    "calculated_land_posterior = np.sqrt(2 * calculated_x0_posterior / calculated_g_posterior)\n",
    "lower_est = round(np.quantile(calculated_land_posterior, 0.025), 2)  \n",
    "upper_est = round(np.quantile(calculated_land_posterior, 0.975), 2)\n",
    "print(f\"The ball landing will be measured between {lower_est} and {upper_est} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Projectile Motion\n",
    "\n",
    "Next, instead of a ball strictly falling, instead imagine one thrown straight upward. In this case, we add the initial velocity back into the equation.\n",
    "\n",
    "$$x_f = \\frac{1}{2} g t^2 + v_0 t + x_0$$\n",
    "\n",
    "We will envision the ball tossed upward, starting at 1.5 meters above ground level. It will be tossed at 7 m/s upward. It will also stop when hitting the ground.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v0 = 7\n",
    "x0 = 1.5\n",
    "x_projectile = (1/2) * g * t**2 + v0 * t + x0\n",
    "noise = rng.normal(0, 0.2, x_projectile.shape)\n",
    "x_obs_projectile = x_projectile + noise\n",
    "df_projectile = pd.DataFrame({\"t\": t, \"tsq\": t**2, \"x\": x_obs_projectile, \"x_true\": x_projectile})\n",
    "df_projectile = df_projectile[df_projectile[\"x\"] >= 0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax.scatter(df_projectile.t, df_projectile.x, label=\"Observed Displacement\", color=\"C0\")\n",
    "ax.plot(df_projectile.t, df_projectile.x_true, label='True Function', color=\"C1\")\n",
    "ax.set(xlabel=\"Time (s)\", ylabel=\"Displacement (m)\", ylim=(0, None))\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modeling this using Bambi, we must include the linear term on time to capture the inital velocity. We'll do the following regression,\n",
    "\n",
    "$$x_f = \\beta_0 + \\beta_1 t + \\beta_2 t^2$$\n",
    "\n",
    "which then maps the solved coefficents to the following: $\\beta_0 = x_0$, $\\beta_1 = v_0$, and $\\beta_2 = \\frac{g}{2}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, Intercept, I(t ** 2), t]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "34d86262d8484668887e38c1fe3e043c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n"
     ]
    }
   ],
   "source": [
    "model_projectile_all_terms = bmb.Model(\"x ~ I(t**2) + t + 1\", df_projectile)\n",
    "fit_projectile_all_terms = model_projectile_all_terms.fit(idata_kwargs={\"log_likelihood\": True}, target_accept=0.9, random_seed=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.202</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2723.0</td>\n",
       "      <td>2328.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>1.561</td>\n",
       "      <td>0.066</td>\n",
       "      <td>1.441</td>\n",
       "      <td>1.687</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2058.0</td>\n",
       "      <td>2550.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(t ** 2)</th>\n",
       "      <td>-4.867</td>\n",
       "      <td>0.114</td>\n",
       "      <td>-5.079</td>\n",
       "      <td>-4.649</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>6.909</td>\n",
       "      <td>0.189</td>\n",
       "      <td>6.553</td>\n",
       "      <td>7.262</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>2039.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "sigma      0.202  0.017   0.171    0.234      0.000    0.000    2723.0   \n",
       "Intercept  1.561  0.066   1.441    1.687      0.001    0.001    2058.0   \n",
       "I(t ** 2) -4.867  0.114  -5.079   -4.649      0.003    0.002    1667.0   \n",
       "t          6.909  0.189   6.553    7.262      0.005    0.003    1694.0   \n",
       "\n",
       "           ess_tail  r_hat  \n",
       "sigma        2328.0    1.0  \n",
       "Intercept    2550.0    1.0  \n",
       "I(t ** 2)    1966.0    1.0  \n",
       "t            2039.0    1.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(fit_projectile_all_terms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial height: 1.43 to 1.69 meters (True: 1.5 m)\n",
      "Initial velocity: 6.54 to 7.28 meters per second (True: 7 m/s)\n",
      "Acceleration: -10.16 to -9.27 meters per second squared (True: -9.81 m/s^2)\n"
     ]
    }
   ],
   "source": [
    "hdi = az.hdi(fit_projectile_all_terms.posterior, hdi_prob=0.95)\n",
    "print(f\"Initial height: {hdi['Intercept'].sel(hdi='lower'):.2f} to {hdi['Intercept'].sel(hdi='higher'):.2f} meters (True: {x0} m)\")\n",
    "print(f\"Initial velocity: {hdi['t'].sel(hdi='lower'):.2f} to {hdi['t'].sel(hdi='higher'):.2f} meters per second (True: {v0} m/s)\")\n",
    "print(f\"Acceleration: {2*hdi['I(t ** 2)'].sel(hdi='lower'):.2f} to {2*hdi['I(t ** 2)'].sel(hdi='higher'):.2f} meters per second squared (True: {g} m/s^2)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We once again are able to recover all our input parameters.\n",
    "\n",
    "In addition to directly calculating all terms, to include all polynomial terms up to a given degree you can use the `poly` keyword. We don't do that in this notebook for two reasons. First, by default it orthogonalizes the terms making it ill-suited to this example since the coefficients have physical meaning. For more information on the orthogonalization, please see the [orthogonal polynomial notebook](https://bambinos.github.io/bambi/notebooks/orthogonal_polynomial_reg.html). The orthogonalization process can be disabled by the `raw` argument of `poly`, but we still elect not to use it because in later examples we decide to use different effects on the $t$ term vs the $t^2$ term, and doing so is not easy when using `poly`. However, just to show that the results match when using the `raw = True` argument, we'll fit the same model as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, Intercept, poly(t, 2, raw=True)]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da31c098659f477fa909267ec3e3d860",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.201</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3066.0</td>\n",
       "      <td>2205.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>1.561</td>\n",
       "      <td>0.067</td>\n",
       "      <td>1.437</td>\n",
       "      <td>1.682</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>2154.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly(t, 2, raw=True)[0]</th>\n",
       "      <td>6.911</td>\n",
       "      <td>0.196</td>\n",
       "      <td>6.556</td>\n",
       "      <td>7.280</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>2092.0</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poly(t, 2, raw=True)[1]</th>\n",
       "      <td>-4.870</td>\n",
       "      <td>0.118</td>\n",
       "      <td>-5.095</td>\n",
       "      <td>-4.653</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>2166.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "sigma                    0.201  0.017   0.172    0.234      0.000    0.000   \n",
       "Intercept                1.561  0.067   1.437    1.682      0.001    0.001   \n",
       "poly(t, 2, raw=True)[0]  6.911  0.196   6.556    7.280      0.004    0.004   \n",
       "poly(t, 2, raw=True)[1] -4.870  0.118  -5.095   -4.653      0.003    0.002   \n",
       "\n",
       "                         ess_bulk  ess_tail  r_hat  \n",
       "sigma                      3066.0    2205.0    1.0  \n",
       "Intercept                  2535.0    2154.0    1.0  \n",
       "poly(t, 2, raw=True)[0]    2092.0    2075.0    1.0  \n",
       "poly(t, 2, raw=True)[1]    2059.0    2166.0    1.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_poly_raw = bmb.Model(\"x ~ poly(t, 2, raw=True)\", df_projectile)\n",
    "fit_poly_raw = model_poly_raw.fit(idata_kwargs={\"log_likelihood\": True}, random_seed=SEED)\n",
    "az.summary(fit_poly_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the same results, where `poly(t, 2, raw=True)[0]` corresponds to the coefficient on $t$ ($v_0$ in our example), and `poly(t, 2, raw=True)[1]` is the coefficient on $t^2$ ($\\frac{g}{2}$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measuring gravity on a new planet\n",
    "\n",
    "In the next example, you've been recruited to join the space program as a research scientist, looking to directly measure the gravity on a new planet, PlanetX. You don't know anything about this planet or it's safety, so you have time for one, and only one, throw of a ball. However, you've perfected your throwing mechanics, and can achieve the same initial velocity wherever you are. To baseline, you make a toss on planet Earth, warm up your spacecraft and stop at Mars to make a toss, then travel far away, and make a toss on PlanetX. \n",
    "\n",
    "First we simulate data for this experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_throw(v0, g, noise_std, time_step=0.25, max_time=10, seed=1234):\n",
    "    rng = np.random.default_rng(seed)\n",
    "    times = np.arange(0, max_time, time_step)\n",
    "    heights = v0 * times - 0.5 * g * times**2\n",
    "    heights_with_noise = heights + rng.normal(0, noise_std, len(times))    \n",
    "    valid_indices = heights_with_noise >= 0\n",
    "    return times[valid_indices], heights_with_noise[valid_indices], heights[valid_indices]\n",
    "\n",
    "# Define the parameters\n",
    "v0 = 20  # Initial velocity (m/s)\n",
    "g_planets = {\"Earth\": 9.81, \"Mars\": 3.72, \"PlanetX\": 6.0}  # Gravitational acceleration (m/s^2)\n",
    "noise_std = 1.5  # Standard deviation for noise\n",
    "\n",
    "# Generate data\n",
    "records = []\n",
    "for planet, g in g_planets.items():\n",
    "    times, heights, heights_true = simulate_throw(v0, g, noise_std)\n",
    "    for time, height, height_true in zip(times, heights, heights_true):\n",
    "        records.append([planet, time, height, height_true])\n",
    "\n",
    "# Convert to a DataFrame\n",
    "df = pd.DataFrame(records, columns=[\"Planet\", \"Time\", \"Height\", \"Height_true\"])\n",
    "df[\"Planet\"] = df[\"Planet\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And drawing those trajectories,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z9O/fn8jISKqrq1myZEmjywIOp/a11r6eH7Jtu+59aOprbYri4uJGZ4U09r6vXLmS7du3061bN55++ul6tRYAdu/e3SIxxsfH89///d/813/9F+vWrWPJkiV8+eWXLFq0iAceeICoqCjOO+88oHWuKRERCS/dXhURkXajdp3usGHDGuzz+/11U+CPRmxsLCkpKQBs2rTpqB9Xm/iuWrUKy7KO6jGHmjpfq7bI2ObNmw85clwbY69evY4y0vrOOecc4uLimDNnDqWlpcdc+K6Wy+Xi7LPPBjjicofG1MZ/qPd827Zt+P1+HA4HPXv2PObzN1Vj68aBumvq4Pe9dnnGgAEDGiTycPQ1G5rKMAyysrK4+uqreeONN7jxxhuBULu8Wkd6nysrK+tuOjT1mhIRkfBSMi8iIu1G7XT6xkZ1//GPfxx2FLIx55xzDgCvv/76UT9m+PDhJCYmsnfv3rpp6kdSW+itdqr+D40YMYLIyEh2795dN3X6YKtWrSI7OxvDMBg3btxRx/rDGC688EL8fj//93//x3/+85+j7i3/Q9OmTcPlcvHtt9/WVeE/lIqKCt555526r8ePHw+EEk+v19vg+JkzZwKh97kp09Gb6lA3Jmq3H/y+116HhYWFDY73+/288cYbLRDhoQ0ZMgSg3vr92oKGn332GQUFBQ0e88477+Dz+ejWrdth61SIiEjbpWReRETajREjRgDwxBNP1Evc582bx4wZMxqtjn4406ZNIyEhgQ8++IBHHnmkwdT50tJS3n///bpWdBBKim+99VYg1Ebu448/rjeaXlZWxmuvvVYvvtpiYqtWrWq0cFxMTExd1fTp06ezdu3aun07duzg/vvvB+D8888/rtHqn/70pwA8++yz+P1+Jk2aREJCwjGfJyMjg/vuuw+Ahx9+mD/84Q8NljhUVFTw3nvvcdFFFzF37ty67RdddBFpaWkUFhZy//3311uv/eGHH9Yl/tOmTTvmuI7Hxo0bmTFjBn6/Hwi1Znv88cdZs2YN0dHR9ZZ1DBkyBKfTyfLly5k9e3bd9oqKCn71q181muQfr3/+858888wzDarIl5SU1N0AOfnkk+u2jx07lkGDBuHz+bjnnnvq3QD79ttvefrpp4HQ+3ykmSMiItI2ac28iIi0G1OnTuWTTz7h+++/54wzzqB3796Ul5eTm5vLmDFjSElJOWRv78Z06dKFv/3tb9x222289tprzJo1i969exMZGUlxcTG7du3Ctm0uuOCCeo+76qqr2LJlC2+99Ra//OUv+cMf/kD37t0pLi5mz549BINBzjrrrLriYwMGDKBXr15s27aN008/nd69e+Nyuejfvz///d//DcBdd93FmjVrWLRoEZdccgnp6ek4nU42btxIMBikf//+PPjgg8f1/g0aNIjMzEzWr18PHPsU+x++BxEREfzpT39i5syZzJw5kx49epCYmEhFRQW7du3C7/fjdDrrbsJAqEDdE088wQ033MCnn37K119/TZ8+fSgqKqqb9n3LLbcwceLE43qtx+quu+7i8ccf5+9//zs9evRg165dlJSUYJomv//97+u1w+vcuTNXX301r7zyCvfddx9//etfSUxMZPPmzQSDQf77v/+bhx56qFnjKy4u5sknn+TJJ58kNTWVlJQUvF4vW7duxe/3k5qayl133VV3vGEYPPbYY1x99dUsXryY008/nX79+lFZWVnXs/7HP/5xo7UnRESkfVAyLyIi7UZaWhpvv/02jz/+OAsWLGDLli1069aNO+64gxtvvLFJye6IESP49NNPef311/n666/ZsWMHlmWRmprKhAkTOOOMM+qm49cyDIOHHnqIM844g7feeovvv/+edevWkZSUxKhRozj33HPr1uNDqAL4888/z+OPP87SpUtZuXIlwWCw3jkjIiJ4+eWXeeutt/jwww/ZsmULlmXRt29fLrjgAq699tom93Y/2KWXXsojjzxyTL3lD+Xyyy/njDPO4O233+bbb79l27Zt7N69m6ioKLKysjjllFO4/PLLG7Q5GzJkCB9++CHPP/883377LevXrycqKorx48dz9dVXt3oiD3DBBReQlZXF888/z9q1a7Ftm7Fjx3L77bczatSoBsffe++9dOnShbfffpudO3dSXV3NKaecwi233HLISv3H49xzz8Xv97NgwQK2bt3Khg0biIyMJCMjg7PPPpvJkyc36Cxw0kkn8cEHH/Diiy8yZ84cNm7ciNvtZtSoUVx++eVcfPHFGpUXEWnHDPtQlXZEREQ6iL/+9a/87W9/46677qqbIn+ievTRR3nxxRe5/vrr66bKn8gmTZpEbm4uX331Fd27dw93OCIiIkdNa+ZFRKTDq6ioAEKtvU5kfr+/bo137fp5ERERaZ+UzIuISIdWWVnJ/PnzAcjMzAxzNOE1c+ZMCgoKGD16NOnp6eEOR0RERI6D1syLiEiHdcUVV7B161bKysrIyMhg+PDh4Q6p1RUUFHDPPfdQWlrKhg0bME2Tu+++O9xhiYiIyHHSyLyIiHRYK1aswLZtzj33XF544QVM88T7s+f1elm8eDFbt26lX79+PPHEE/Wqy4uIiEj7pAJ4IiIiIiIiIu3MiTdEISIiIiIiItLOKZkXERERERERaWdUAA8oKSlp1vPFx8dTVlbWrOcUCRddz9JR6FqWjkLXsnQUupalI2nu6zkxMfGIx2hkvgWciAWWpOPS9Swdha5l6Sh0LUtHoWtZOpJwXM/6CRIRERERERFpZ5TMi4iIiIiIiLQzSuZFRERERERE2hkl8yIiIiIiIiLtjJJ5ERERERERkXZGybyIiIiIiIhIO6NkXkRERERERKSdUTIvIiIiIiIi0s4omRcRERERERFpZ5TMi4iIiIiIiLQzSuZFRERERERE2hkl8yIiIiIiIiLtjJJ5ERERERERkXZGybyIiIiIiIhIO6NkXkRERERERKSdUTIvIiIiIiIi0s4omRcREREREZF6li1bxtixY6moqAh3KHIIznAHICIiItLqbAuzcBN4y8ATj9UpHQyNcYjIiWX69Ol8+umnADgcDlJTUzn99NOZNm1a2GJ68cUXmTdvHjNnzgxbDO2FknkRERE5oZh52biyZ2EWb4agDxxurKS++IdNxkobFu7wRERa1dixY/ntb39LIBBgxYoV/OlPf6K6upqzzjor3KHJESiZFxERkROGmZeNZ+4MqCnDju4MTg8EvJj5OXjmzsA78V4l9CJyQnG73SQnJwNw7rnnsmzZMubNm9cgmS8rK+PRRx9lxYoVlJeX0717d6655hrOOeecumNuueUW0tPTcbvdfPTRRzidTi655JJ6I/2VlZU89dRTzJs3D6/XS1ZWFnfffTf9+vXj448/5uWXXwZCNxkAfvOb33DRRRe19NvQLimZFxERkRODbeHKnhVK5BN6gmGEtrujsF09MEp34loxC2/XIZpyLyInLI/HQyAQaLDd6/XSv39/rrrqKqKjo5k/fz4PP/wwaWlpDBw4sO64Tz/9lCuvvJKXXnqJ1atX8/vf/57BgwczZswYbNvmnnvuIS4ujscff5zo6Ghmz57N7bffzrvvvstZZ53Fli1bWLhwIU899RQA0dHRrfba2xv9pRIREZETglm4CbN4c2hEvjaRr2UY2NHJmEWbQ2vpRUROQGvWrOGLL75g5MiRDfalpKQwefJkMjIy6NatGz/72c8YM2YMc+bMqXdceno6U6dOpWfPnlxwwQVkZWWxdOlSIFRUb/PmzfzpT38iKyuLnj17cueddxIbG8t//vMfIiIiiIyMxOFwkJycTHJyMhEREa3y2tsjjcyLiIhI29Wcheq8ZaE18k5P4/udEbCvKHSciMgJYv78+ZxxxhkEg0ECgQATJkzgl7/8JVu3bq13XDAY5I033uDLL7+koKAAv9+Pz+cjMjKy3nHp6en1vk5OTqakpASAdevWUV1dzbnnnlvvGK/Xy65du1rg1XVsSuZFRESkTWr2QnWeeHC4IeAFd1TD/YGa0H5P/PEHLyLSTgwfPpx7770Xp9NJ586dcTpDKeIPk/k333yTt99+m7vvvpv09HQiIiJ44okn8Pv99Y6rfXwtwzCwLAsAy7JITk7mb3/7W4M4YmNjm/NlnRCUzIuIiEib0xKF6qxO6VhJfTHzc7BdPepPtbdtjKoirNSs0Oi/iMgJIjIykh49ehzxuBUrVnDaaadx/vnnA6HEfOfOnfTq1euonyszM5Pi4mIcDgdpaWmNHuNyueqSfzk8rZkXERGRtuWHhercUWA6QoXqEnpATTmuFbPAPsYPe4aJf9hkiIjDKN0JviqwguCrCn0dEYd/6GQVvxMRaUSPHj1YvHgxK1euZOvWrfz5z3+mqKjomM4xevRoBg4cyH333cfChQvJy8tj5cqVPPfcc+Tk5ADQtWtX8vLy2LBhA6Wlpfh8vpZ4OR2CRuZFRESkTTmWQnVW54xjOreVNgzvxHsPTN/fVxSavp+ahX+o+syLiBzKddddR15eHnfffTcej4ef/OQnTJw4kcrKyqM+h2EYPP744zz33HP88Y9/pKSkhOTkZIYOHUpSUhIAZ5xxBl9//TW33XYbFRUVak13GIZt23a4gwi32oIMzSUxMbHZzykSLrqepaPQtdx+mLuW4PnyIey4bqER+R+yghjluXjPegir+6imPUlzFtZrZbqWpaPQtSwdSXNfz4mJiUc8RiPzIiIi0ra0RqE6wzzmUX0REZG2pH3cghYREZETRm2hOqOqEH44gbC2UF1y3xOvUJ1tYRZswNq2ALNgw7HXDBARkQ5FI/MiIiJN0Y6nabd5+wvVeebOwCjdiR2dHOoBH6jBqCo6IQvVHdymL0gQD47ja9MnIiLtnpJ5ERGRY9Ts/c/buxa4saFCdQc0aNMXGYNdXXlcbfpERKT9UzIvIiJyDFqi/3l71pI3Nqy0YXi7DjmxZ0D8sE2fYWDUtulz9cAo3YlrxSy8XYecWO+LiIhozbyIiMhRa6n+5+1U7Y0NM38tticOO64btieu7saGmZd9/E+yv1Cd1X1UqGDdCZawHkubPhERObGcWH8RRUREjoMSq4Poxkbr8JaFZjw4PY3vd0aE9nvLWjcuEREJOyXzIiIiR0uJVR3d2GglB7fpa0xztOkTEZF2SWvmRUREjlZr9D9vL47mxsa+omO7sRH0g7cCw1uGUVOB4S3HqCkLbbMCh37cD9vXHQvTDC0R8MRhR4T+jycWOyL+0K+tFdW26TPzc7BdPerfOKlt05eadeK16RMRESXzIiIiR0uJ1UGO9saGKwaqS0OJubfiQHJeU/aDbeUY/urWfx2H43AflOjH7k/047A9sQcS/9r/t1Ti30ibPtsRC76qE7ZNn4iIhCiZFxEROVqt1f+8HfSwr7uxsXctdkxnjEBNKLG3AhiWH6pLwRODe8GTx3Ziw1GXKNueWIiID/3f4T7sw+wfTvWvf9JD7wn6QzcSvBUYNeX7bzCUh2YdBH0Y+wox9hUeOW6HZ3/coVF92xOLHdMFO74bVnx3cDVyw+MoNWjT5y3FwHFCtukTEZEDlMyLiIgcg5buf96me9jXlGGW7cIo24VZtguCXszqIqjai+1wgeEAOxhKkE0nVkzq/gcaB01hrx3ljq8b4ebgKe6uqIZr8MMh4D0wi8BbXj/Rr9mf/O/fjuWHoBejqgCjqqDR09mRSVjx3bHju4f+H9et8XoDh3Bwm75Yt8U+n9kmb/KIiEjrMWz7eBaadQwlJSXNer7ExMRmP6dIuOh6lo6i2a/lFhg9P1QPe6OqECLiWq+HvRXAqNyLWZa7P3HfiVGWi+GraHCoUVOGUbEXgl7AwHZ6sOO74z/5x1jdR4eSdXd0x006bTv0PfKWHzTCX4ZRU4pRnhd6D6uLG3+sw4MV1w0rvlu9JP9IU/bbw7UscjT0GUOa28cff8wf/vAHfvOb33DRRRe16nM39/WcmJh4xGM0Mi8iItIU+/ufN5sftnqrHbF1R2G7emCU7sS1YhberkOaN9HyVYUSzvJdB0bdy/PADjZysIEdk4IVV5t89sCK7waeOMyizSdmMmgY4IrAdkVATAqNjpD4qjDLczHKckPvcXlu6D0OejFLtmCWbKl3uB3duS6xrx3NtyOTWmTGQpueCSIibVJeXh6XXnrpYY/p168fM2fObJHnHzt2LMOGDePZZ59tkfO3J0rmRURE2oBjafXW5JsI/hrMwg2YJdsOJO/VjY8i2M4I7Li0/clkKGm3Y7uBs/G16816Y6OjcUdjdcqAThnU3SKxghhV+ftnPtQm+btCo/pVBTiqCiAvu+4UtiuyLrm3egyEiG4QmXBcYR1qJoiZn4Nn7ozWmwkiIu1S9+7dOffccxvdl5yc3MrRnJiUzIuIiLQFLdHqzbYxynZi5q/FsXctZvGWRkfcG6znju+OHdWpbaxd76hMB3ZsV+zYrljdRx3Y7q3ELD9Ql8Aoy8Ws2I3hr8Yo2oRZtAlr53wi/P7QFP2ULKyUk7GS+x6xSGA94ZoJIiIdRvfu3Zk2bVq4wzihKZkXERFpC5qrh31NGY78HMz8tZj56xqsc7ejOmF1yjgoee92XJXWpZl5YrA694fO/Q8axQ9gVOzdn9zvxF21C/ZswCzPxSzPhU1fgunC6pRBMCULK/Vk7Jguh70Z0yozQUSkySzLZus2qKiA2Fjo3QtMs33eYP3666/56quvyMnJoaCgAKfTSXp6OldccQWTJk2qd2ztFP4LLriAq6++mmeffZYVK1ZQVlbGb37zG/7whz8AkJ2dzdixY+se19ga+SVLlvDiiy+yYcMG3G4348eP56677iI+/gh/R9sRJfMiIiJtQJN72Af9mMWbMfeuDSXw5bn19zs8BDtnhkZvU7KwY1Ja/sVI8zKd2PHdCMZ3A8bgSEykZs9OzIIcHPlrMfNzMGrKMPPXYOavgdVgRyZidc4KJfcpWaEihAdriZkgItIsVq22+fsHNtu3g88PbhecdBL89BIYNLD9JfTPPvssTqeTwYMH06lTJ0pKSvj222/5r//6L+655x5+9rOfNXjMrl27mDp1Kr179+aCCy6gvLycnj17csMNN/Dyyy/TpUsXLrzwwrrjMzLq33T89ttvmT9/PuPHj+fSSy9lxYoVfPrpp+zatYsXXnihxV9za1EyLyIi0hYcbQ97DIyKPQemzhduCLVGO3AirIQedcm7ldQHTP2573A8MVjdR4Wm6Ns2RkUeZn5O6Joo2oRRXYJjx3c4dnxH6JroedA10bv5ZoKISLNatdrm6b/ZVFRCchJ4POD1wsaN8PTfbG6/te0k9Lt27eLFF19sdN/AgQM55ZRTAHj88cfp1q1bvf379u1j2rRpvPDCC1x88cVERETU279y5Uquv/56brzxxnrbBw8ezMsvv0zXrl0PO8X/m2++4W9/+xtDhgwBIBgMcscdd7B8+XJWr17NwIEDj/n1tkX66y4iItJGHLKHfed+BHuMxbF3Da5V7zdodWZHxGOlZBFMOTk0RdsTG6ZXIGFhGNhx3QjGdSOYfhYEfZhFmzDzc0IzNiryMEu3Y5Zuhw3/wnZGYHXqh+2OxajYg53U++hngohIi7Gs0Ih8RSV0SzvwYxkZCWlpkJcH//jAZsDJbWPK/a5du3j55Zcb3XfFFVfUJfM/TOQBoqKiuPDCC3nyySdZu3Ytw4cPr7c/OTmZ6667rsmxnXvuuXWJPIDD4eCCCy5g+fLlrF27Vsm8iIiIND8rbRjeLoNxbJmLuXcVZsUejJpSnFvnHjjIdGIlp4eS95STsePSVKxODnC494/CnwwDfwrVpTgK9if2BeswfJU49qzCsAOYNaXYucuxo5KwIxPAdGLsKzkwE0TF70RazdZtsH17aES+kVIWJCbBtu2h4/r2CUeE9Y0dO5YnnnjiiMcVFxfzxhtvsGDBAvbs2YPX6623v7CwsMFj+vXrh8vlanJsmZmZDbalpISWmVVWVjb5vG2NknkREZE2wqjMx7FzEY6dizD2FdXbZ8d2qUvereR+h2wRJ9JAZALBnqcQ7HlKaNS9dPv+Iok52IaJWboTc18hVBVgmw7shJ74hk7G6jo03JGLnFAqKkJr5D2HKGXhcUOJP3Rce1FWVsb111/Pnj17GDx4MKNGjSI2NhbTNNm4cSPz5s3D5/M1eFxSUtJxPW90dHSDbQ6HAwhNue8olMyLiIiEk38fjl3LcOxcGGodt5/tjAgl7qknE0w5GSITwxikHBPbwizcFCoe54kPTVVvKyPchoGd2ItAYi/IPB/8NZgF63BunYu5dw2GvwbbHYVr/Sc4dy4i2HMswR6jQ1XvRaRFxcaGit15vaGp9T/k9YX2x7ajlVQfffQRe/bs4aabbmowbf6NN95g3rx5YYqsY1AyLyIi0tqsQKhY2Y6FOPasBCuwf4eBlXIygZ5jsLoM0eh7O2TmZR+oeRD0haa8J/XFP2wyVtqwcIfXkCsCK20ovrShYNuYhRtw7FyIIzcbY18hznUf41z3MVZS31Bi3224WhmKtJDevUJV6zduDK2R/0EpC0qKIaNf6Lj2Ijc31GFlwoQJDfatWLGiSec0TRPLso4nrA5DybyIiEhrsG2Msl2hRGnnknr9363YtFCi1H0URCa0YAxteMS4AzDzsvHMnQE1ZaGRbKcHAl7M/Bw8c2fgnXhv20zoaxkGVudMrM6Z+Af/HMfu73HsWIhZsC7U/rB4M66V7xDsMphgz7GhlnfqlCDSbEzT4KeXhKrW5+WF1sh73KER+ZLi0Ij8pZcYbaL43dHq0qULAN9//z3p6QcKan7++ed89913TTpnXFwc+fn5zRJfe6ffwCIiIi2puhTHriU4di6q1wPedscS7DGKYI+x2PHdW7yAXbsbMW5vbAtX9qxQIp/Q88D30x2F7eqBUboT14pZeLsOaR83UJwegj1GE+wxGqpLQtfwjoWYFbtx5C3Hkbf86K5h3UASOSaDBhrcfit1feZL9veZz+gXSuTbSls6OHxrOoBp06Zx/vnnM3PmTB5//HGWL19Oly5d2LRpE0uWLOH000/n66+/PubnHTFiBF999RUPPPAAGRkZOBwOTj311Ho3C04USuZFRESaW8CHued7nDsWYeavBezQdtMZllHNdj9i3A6YhZswizeH3t9GylDb0cmYRZsxCzdhdc4IT5BNFZlIsN85BNPPbjC7xLl5Ds7Nc7DiuhHsMabe7BLdQBJpmkEDDQacHKpaX1ERGpHv3atttKM72OFa00EomU9JSeHZZ5/l6aefZsmSJQQCATIzM/nrX//K3r17m5TM33PPPQAsW7aMuXPnYlkWSUlJJ2Qyb9i2bYc7iHArKSlp1vMlJiY2+zlFwkXXs3QULX4t2zZm0UYcOxZh5i3HCNTU7bKS+hDssX+9sbthhd0WZVt4Pr0XM39t/RHj/TEbpTuxUrPwnj9DI6bHwdy1BM+XD2HHdQPT0fAAK4hRnov3rIewuo86rudqE7+XD1v3IQsrKhnX2g/BW1HvBpJRVQgRcbqBJEAbuZZFmklzX8+JiUcufKuReRERkeNQ105ux0KM6uK67XZk0v5K4GOwY1LCFl+HHjFuSzzx4HBDwAvuRgrEBWpC+z3xrR9bSzCdWF0GYXUZhN9XhSN3eV1HBnPvGpz5ORi+SqzYroAVulHUXpcciIi0UUrmRUREjpVtY+avxbnx35iF6w9sdkZgpQ0n2HNMqBd8C6+DPyrestAUZ+chGhc7I2BfUeg4aTKrUzpWUt9Q73ZXj4YzIKqKsFKzQmvGOxp3NMHeEwj2noBRmY9z7YeYu1dgO9wYNaUYNaXgcGNHd8KOTNQNJBGRZqJkXkREOrb9BbisMgvTZx5fAS4rEOoJv+nfBxWza+Pt5E60EeNwMUz8wybjmTsDo3QndnRy6EZJoAajqggi4vAPndzhR6LtmBSCaUNxRiZhRyZieMsxakI3lIzyPIzKvdiRSaHrTjeQRESOi5J5ERHpsA4uwBUkiAdH0wpwBbw4tn2Lc/NXGNX718M5PAR6jSfQdxJEJbXMC2gGJ/SIcSuz0obhnXjvgaJv+4pCRd9Ss/APPYGKvnniQzNBTCd2fHfs2K4Y1SWh9fKWH6NiN4Zl4dixCDupD3ZUcrgjFhFpl5TMi4hIh9SggntkDHZ15bFVcK8px7nlPzi2zsXwVwNge2IJ9J1EsNeE1i9m1xQaMW5VVtowvF2HnNDt2BrcQDIdoSn2UUlQXYZZshXbFYlj72oc/36QYLcRBNLPxk7oEe7QRUTaFSXzIiLS8TTS89swHUddgMuo2Itz85c4diysq9Jtx6QSSD+bYI9RoWnp7YhGjFuZYZ7Ya8EPdwPJW4Gd2BvfkCtwlO7ALFgX6mG/awlW5/4E+p2D1bl/26g3ISLSximZFxGRDqepFdyN4q04N36BY/f31PaGtxJ7hxKMrkPadYKhEWNpTUdzAykIGKU7Qz9zecsxC9bhLliHFd+DQL+zsdKGN97mT0REACXzIiLSER1LBXfbxty7GufGLzCLNtUdEuwyiEC/c7CT+rbrJL6eE33EWFrV0dxAshN64B91A4F9P8Gx6Suc2+djlu3EvfQV7MjZBNLPInjSqQ1/lvcXttSNKRE5kSmZFxGRjudoKribLszibbjWf4pRsSe03XAQ7DGGQL+zsGO7tm7MIh3RUd5AsqOSCQz+GYH+F+Lc8jWOLXMxqotxrXoX57qPCfaeSKDvGeCJrVfYkqAvNOLflMKWIiLtXJtN5idNmkRubm6j+6644gqmT59eb1tlZSVPPfUUX3zxBQUFBXTu3JlzzjmHO+64g5iYmNYIWURE2ojDVnAPBjBKdmA4nDg3fxWadu+MINj7NAJ9TofIxLDFLXLCc0cT6H8hgX5n49ixEOemLzGqCnBu+BfOTf/Giu+OY+cS8O8LLaNxeiDgPbbCliIiHUSbTeYBYmNjueaaaxpsHzhwYL2v9+3bx5QpU8jJyWHcuHFceOGFrFu3jtdee41Fixbx5ptvEhXVyMiMiIh0TI0U4LKJwCjZhVGZj2E6sOL7Ykcm7K9MPx5c+jsh0mY43AR7n0aw13jM3d+HlsEUb8W57lMMXyVWTGrouGMobCki0tG06WQ+Li6OO+6444jHvfTSS+Tk5DB16lR+/etf121/8skneeaZZ3jppZe48847WzJUERFpY+oKcC15Fcee78FfhQnY7mgCXQcTGHIlwe6jwOEKd6giciiGiZU2DF/XoTg2foln73RshxvDW47hLQd3dCixd0cfsrCliEhH1e5vW9q2zXvvvUdUVBS33XZbvX033XQT8fHxvP/++9i2HaYIRUQkLPz7MEu2Yxg2dsJJkJpFoN+51Fz8JN4fPRkqqqVEXqR9MAzsqATsiDisTv2wIxMAA3xVmMVbMEq2h74O+kJF8URETgBtemTe5/PxwQcfsHfvXuLi4hg+fDj9+/evd8y2bdvIz89n/PjxDabSezweRo4cyVdffcX27dvp1atXK0YvIiJhYQVwbPumbjouQDBtKJ6xV1Hp6hzm4ESkyWoLW2Jgx/fAjkkNLZupLgmN1FeXhNbQG236461Iu5eXl8ell14KQOfOnZk9ezYOR8M2kps2bWLKlCkAnHTSSbzzzjutGueJoE3/tisoKOD++++vt23ChAnMmDGDpKQkALZv3w5wyET9pJNOqjtOybyISAdm25i7V+BaMxujKj+0KSYV/4BLsLoMJjopCUpKwhykiDRVg8KWDjd2fHfsqE4YFbsxqwqwHW5c2W8QqMon2PeM/cm/iLQEh8NBQUEBixYt4tRTT22w/6OPPsLhcBAMBsMQ3YmhzSbzl156KaNHjyY9PR23283mzZt5+umnmTdvHrfeeitvvfUWhmFQUVEBcMiK9bXba49rTHx8PKbZvCsOEhNVDVk6Dl3P0tbZBZuwlr+JXbAxtCEmGXPIpRjppxNlHhgt0LUsHcWJei1bp91M8LOHoCIXYlLAFQGWCU4XJPXC7NQXR6AG98ZPIHcB5tDLMXqPwzi4o4W0KSfqtdyeVVVVATB8+HDWrVvH559/zoUXXljvGJ/PxxdffMHEiROZM2cOpmmeEN/r1n6NbTaZv/322+t9PWTIEJ5//nmmTJnCsmXLmDt3LqeffnqzPFdZWfOurUpMTKREoz/SQeh6lrbMqCrAuWY2jrzloQ2mi0D6WQT6nRP6kF9WXnesrmXpKE7oazk2HXPcLxv2me+UiX/oZKyuQ3HsXIwz50OMsr0w92msFR8SGHAJVkpWuKOXHzihr+V2rDZ3cjgcnHnmmXzyySds3bqVhISEumPmzJlDSUkJ5557LnPmzMGyrLrvdUFBAbNnz2bhwoXk5eVRWVlJcnIyp556KlOnTq2bgV1r+vTpfPrpp/z9739n3rx5/POf/2TXrl2cffbZPPjgg3i9Xt5//33+9a9/sXv3bizLIikpiZNPPplrrrmG9PT0Vnlfmvt6PpobA202mW+MaZpceumlLFu2jOXLl3P66acTGxsLhPrMN6Z2e+1xIiLSAfiqcK7/FOeWuWAHAYNgz7H4s36kPvEiHZyVNgxv1yGYhZtCxe488Vid0uva0QV7jiHYbRiOzf/BueEzzLKduL97EitlAP6Bl2DHdQvzKxA5CrZ1yGu8LbnooouYPXs2n3/+OVdccUXd9o8++ojExETGjRvX4DErVqzgzTffZOTIkQwYMACn08mGDRv4xz/+waJFi3j99dcbnXX92GOPsXr1asaNG8e4cePqkv7p06fz1VdfkZ6ezkUXXYTL5WLv3r0sW7aMMWPGtFoyHw7tKpmHA3coqqurgQNr4rdt29bo8bVr6muPExGRdizow7FlLs4N/8Lwh/4OWJ2z8A+8FDu+e5iDE5FWY5iHbz/ncBPMOJfgSeNCN/62zsPMX4NnzlqCPU/Zf+MvodXCFTkWZl52w9knSX3xD5uMlTYs3OHVM3DgQPr06cNHH31Ul8zn5+ezePFirrjiCpzOhunmiBEj+OSTTxoUL//000+ZPn067733Htddd12Dx23atIk33niDLl261G2rrKxkzpw5ZGVl8dJLL9UrxBcMBtm3b19zvdQ2qe3d3jmClStXAtCtW+iuaq9evUhJSWH58uUNvller5elS5eSkpKiZF5EpD2zbcxdS/B8+TCuNf/A8FdjxXXDd+od+MbdqUReRBrniSEw+Gd4z3yQYNpwwMax4zsivvwdzpyPwF8T7ghF6jHzsvHMnYGZvxbbE4cd1w3bE4eZnxPanpcd7hAbuPDCC9m0aRPr1q0D4JNPPiEYDPKjH/2o0eOTkpIaJPIA559/PtHR0SxZsqTRx02ePLleIg9gGAa2beNyuRpU1Hc4HB1+dnabTOY3bdpEeXl5g+1Lly7l1Vdfxe12c8455wChb+Dll1/Ovn37eOaZZ+od//zzz1NWVsbll1+uwiciIu2UWbgB99y/4F76CkZ1MXZEPP7hV+M747+wUk4Od3gi0g7YMSn4R0/De9qvsZL6QNCHc/2neL58EMfWeWCp2ra0AbaFK3sW1JRhJ/QEdxSYDnBHYSf0gJpyXCtmgW2FO9J6zj//fJxOJx999BEQSuYHDBhA7969D/mY//znP9x1112cd955jBs3jrFjx3LKKadQVVVFYWFho48ZMGBAg23R0dGMHTuWlStXcvXVV/Paa6+xYsUK/H5/87y4Nq5NTrP/17/+xUsvvcQpp5xCt27dcLvdbNiwgfnz52OaJg8//DBpaWl1x0+dOpU5c+bw0ksvkZOTw4ABA1i3bh3z5s0jKyuLqVOnhvHViIhIUxgVe3Cu+QDHntCMLBweAhnnEuh7JjjVbkpEjp2d1AffhF/tb2P5AUZVAa7v38K5eU5dG0s0ACRhYhZuwizejB3dueF1aBjY0cmYRZsxCzcdfplJK0tKSuLUU0/l3//+NxMmTGDXrl1Mnjz5kMfPmjWLp556isTEREaPHk1KSgoejweAd955B5/Pd8jnacwjjzzCa6+9xr///W+ee+45AKKiovjRj37ELbfcQkRExHG+wrarTSbzY8aMYfPmzaxdu5bFixfj8/lITk7mggsu4Nprr2Xw4MH1jo+KimLmzJk8/fTTfP755yxevJhOnTpx7bXXcvvttzc6jUNERNqomnKc6z/Bue3b0OiDYRLoNZ5A5oUQERfu6ESkvTOMUBG9LoNwbP0G57pPMCr34l70HFZyv1ANjsRe4Y5STkTestAaeaen8f3OCNhXFDqujbnooouYN28ef/zjH/F4PHWzqH8oEAjw6quv0rlzZ9544416Fdtt2+b//u//jvm5IyMjueWWW7jlllvIy8tj2bJlfPDBB7zzzjt4vV7uv//+Jr+utq5NJvOjR49m9OjRx/SY2NhYHnjgAR544IEWikpERFpUwIdz81c4N3wOQS8AwS6DCQy4BDu2yxEeLCJyjEwnwb5nEOw5BueGL3BunoNZtBHP3L8Q7DYS/4BLIKrxkUCRFuGJB4cbAt7QFPsfCtSE9nviWz+2Izj11FNJTk6moKCA8847j+jo6EaPKysro7KykpEjRzZovZaTk4PX6z2uONLS0khLS+Pss8/mggsu4JtvvlEyLyIi0pLMgvW4VszCqCoAwEo4icDAS7E6tZ1phCLSQbmiCAz4CYE+E3Gt/RDHzsU4cpfi2LMK/8k/JthnYptsCSYdj9UpHSupL2Z+DrarR/2p9raNUVWElZoValPXxjidTv7nf/6HgoICMjMzD3lcYmIiHo+H9evXU1NTUzcFvry8nMcee+yYn7ekpIS8vLwG6+krKirw+XwkJCQc8znbEyXzIiISPr4qXKv/gWPHdwDYEQn4B16K1W2k1q2KSOuKTMQ/4loCfc/EtfIdzOLNuFa9i2PXUvzDp2DHdg13hNLRGSb+YZPxzJ2BUboTOzo5NLU+UINRVQQRcfiHTm6zN5dOPvnIRWlN0+SnP/0pb775JlOmTGH8+PFUVVWxcOFCunTpQufOnY/pOQsKCrjhhhvo06cPmZmZdO7cmbKyMubNm0cgEGDKlClNfTntgpJ5EREJCzMvG9f3b2N4Q91LAr1PI3DyT8AVGd7AROSEZif0wDfhlzi2zsO5djZmyRY8c/5IIPN8AhnngqmPz9JyrLRheCfee6DP/L6iUJ/51Cz8Q9ten/mmuPXWW4mLi+OTTz7hH//4B0lJSZx11llMmzaNX/ziF8d0rq5duzJ16lSWLVvGkiVLKCsrIyEhgczMTK688krGjBnTQq+ibTBs27bDHUS4lZSUNOv5EhMTm/2cIuGi61maXXVpaLRrf69cOyYV37Ap2MktO21Q17J0FLqWW1F1Ca7v38KxZxUAVmxX/MOuwk46dMstOXq6lg/DtjALN4WK3XniQ1Pr2+iIvIQ09/X8w5oCjdGtRRERaR22jWP7dzjX/B3DXw2Gg0C/swlknh8q6CMi0tZEJuIfcwvB3GW4Vr6LWbEbz7z/IdDndAIn//jQVcdFjpdhtqn2c9I2KZkXEZEWZ1Tm41rxJmbheiBU4M4/bAp2fPcwRyYi8gONjIha3UfiTcnCtfp9HDsW4tzyHxy7v8c/9BdYqQOOfE4RkRagZF5ERFqOFcSx+StcOR+D5QfThT/rRwT7TgLTEe7oRETqMfOyD6xVDvpCa5WT+uIfFlqr7B9+DcHuo3Blz8KoLsa94GmCPcbgH3gZeGLCHb6InGCUzIuISIswSnfiWvF/mKU7ALA6ZeIf+gvsmJQwRyYi0pCZl41n7gyoKcOO7hyaQh/wYubn4Jk7A+/Ee7HShmGlnIz3zAdx5vwT5+b/4Ni5CHPvWvyDL1cnDhFpVUrmRUSkeQV9ONd/inPjv8G2sF2RBAb+lGDPU/UhV0TaJtvClT0rlMgn9Dzwu8odhe3qsf/m5Cy8XYeEipA5PQQGXU6w+0hcy/8PsyIP99JXCO5cjH/oLyDyyIWrRESOl5J5ERFpNmbhBlwr3sSo3AtAMG0Y/kE/g8iE8AYmInIYZuEmzOLNoRH5H950NAzs6GTMos2YhZvqFSWzE3vjO+MBnBu/wLnuUxx7V+P48mH8A35CsPfE1rmBqarnIicsJfMiInL8/PtwrpmNc9s3ANgR8fgHX9Eh+uGKyAnAWxZaI3+o6vTOiFC/b29Zw32mk0DmBQTThuHK/j/M4i24Vr6DY9eSUKHP2K4tFvaR1viLSMem23YiInJczN3f4/nq93WJfPCkcXjPfFAfJEWk/fDEh1pkBryN7w/UhPZ74g95Cju2K74Jv8I/+Ofg8GAWb8Ez5484130CVqDZQ65d42/mr8X2xGHHdcP2xNWt8Tfzspv9OUWkbdHIvIiINE1NeWj0KW85AHZ0Z/xDJ2N1zgxzYCIix8bqlI6V1BczPwfb1aP+9HjbxqgqwkrNCk1hPxzDINhnIsGug3GteBPH3tU4132Mmbs8NEqf1Lt5Aj7WNf4i0iHpp1tERI6NbePYsQDPV9NDibxhEuh3Dt5Jv1EiLyLtk2HiHzYZIuIwSneCrwqsIPiqQl9HxOEfOvnoE+PIRPxjb8U38npsdyxmRR6eef+Dc9V7hx79PwbHssZfRDoujcyLiMjRqynDvXwmZv4aAKz4HqHRpoSeYQ5MROT4WGnD8E6898Aa9H1FoTXoqVmhWUfHunTIMLC6j8KbcjKuVe/h2LkI5+Y5OHZ/j2/kDcc3Sn88a/xFpMNQMi8iIkfFzM/BtfRVDF8FmE78/S8imH4mmPpTIiIdg5U2DG/XIc1bHd4djX/EtQS7jwp1+9hXhOebx/CffDHB9LObVvH+4DX+7qiG+49ijb+ItH/6BCYiIodnBXDmfIxz4xeAjRWbhn/01Bat0CwiEjaGWa/9XHOxUgfgnfTfuLJn4chbjmvNBzgK1uMbcS14Yo/tXM21xl9E2jWtmRcRkUMy9hXh/uZ/cW78HLAJ9hqP7/T7lMiLiDSFKwr/qKn4h04B04WZvxbPnD9gFqw7tvM09xp/kRby4osvMnbsWJYtWxbuUDok/YSLiEijzLxs3P/5I2bJFmxnBL5RU0MfDh3ucIcmItL+2BZmwQbM3KXY0Z3xTrwXK7Yrhrcc9/wnca79MJSQH6XaNf5WShaGtwKjPBfDW4GVmhXarvag0kLy8vIYO3Zsvf/Gjx/PxRdfzIMPPsjGjRvDHeIxGzt2LLfcckuj+x577DHGjh3Lyy+/3Oj+7du3M3HiRE4//XSqqqpaMswGNM1eRETqC/pwrv47zq3zALASe+EfeQN2dKcwByYi0j6ZedkHCusFfaHCekl98Q/+GY6ijTi2fYtzw2eYhRvwjbwBopKO6rwtssZf5Ch1796dc889F4Dq6mpWr17NF198wddff83TTz/N4MGDwxxh87jttttYuHAhr776KuPHjycz80DnnmAwyMMPP4zP5+NPf/oT0dHRrRqbftJFRDqi2hGgXUswCzaAbR3Vw4yKPbjnzqhL5AP9zsE34ZdK5EVEmsjMy8YzdwZm/lpsTxx2XDdsTxxmfg6eb/+XYMrJ+EZNxXZGYBZvwfOfP2DmZR/9E+xf4291HxVa669EXlpJ9+7dmTZtGtOmTePOO+/khRde4Nprr8Xn8/Hcc8+FO7xmExERwYMPPohlWUyfPh2/31+374033mDt2rVccsklnHrqqa0em0bmRUQ6mEOOAA07TGsl28axcyGu79+GoA/bHYt/xDVYqQNaN3gRkY7EtnBlz4KaslALz9pCde4obFcPjNKduFbMwnv+DHwJJ+Fa+jJmyTbci18g0HsigYGXamnTCcqyLbaVrKfSV0qMO4FeiZmY7eBGzeWXX85rr71GTk7OYY/76KOPmDdvHhs3bqS4uJiIiAiysrK4+uqrGTFiRL1jly1bxm233cYNN9zAhAkTePbZZ1m1ahWGYTBy5Ejuuusu0tLSGjxHXl4er776KosXL6a4uJi4uDjGjBnDtGnT6Nq1a71zA2RnZzN27Ni6x//mN7/hoosuAmDQoEFceeWVzJo1ixdffJFbb72VjRs38sorr9CtWzfuuOOO43rfmkrJvIhIB1I7AkRNGXZ051AP4oA3NAI0d0bj6yj9NbhWvo1j5yIArE6ZoerKkQmtHr+ISEdiFm7CLN4c+n38wxZ0hoEdnYxZtBmzcBNW5wx8E36Jc+0/cW76N86tczGLNuEfNRU7tkt4XoCExZq9S5id8wo7SjfiD3pxOTz0TOjHT7KuZ0DqqHCHd1jGUbZafPTRR0lPT2fUqFEkJiZSUFDA3LlzueOOO/jzn//Maaed1uAx69atY9asWQwfPpyf/OQnbNiwgblz57J582ZmzZqFx+OpO3b16tXcfffdVFdXM378eLp3786ePXv4/PPPWbBgAS+99BLdunWja9eu3HDDDbz88st06dKFCy+8sO4cGRn1u1rcdNNNLFiwgFmzZnHKKafwv//7vwSDQX7zm98QGRnZxHfs+CiZFxHpKI52BKjrkLppmEbpTtxLXsKoygcMAlk/IpBxrqZpiog0B29ZaIaU09P4fmcE7CsKHQdgOgkMvBSrcyauZa9jlufi+foR/EN+TrDH2Kb1pJd2Zc3eJTy3+CEqfWUkRabgcUbgDdSwqWgVzy1+iJtHP9SmE/p3330XgKysrMMe99ZbbzUYTS8sLOS6667jqaeeajSZnz9/Pr///e85++yz67Y9/PDD/Otf/2LevHl12wOBAL/97W+xLIvXXnuNfv361R2/YsUKbrvtNh5//HEee+wx0tLSmDZtGi+//DJdu3Zl2rRph4zZ7Xbz4IMPcsMNN3DXXXfh8/n4+c9/zrBh4Ss2qU9rIiIdxLGMAGHbODb/B8/cv2BU5WNHJuKdcA+BzPPDk8g3cY2/iEib5okPTZMPeBvfH6gJ7ffE19tspQ7Ae8Z/YXXKhKAP1/I3cC1/Hfw1rRC0hItlW8zOeYVKXxlpsb2IdEVjGg4iXdGkxfai0lfGhzmvYrWRv5G7du3ixRdf5MUXX+TJJ5/kxhtv5LXXXsPj8RyyMnytxqbFd+rUidNPP52dO3eye/fuBvuHDRtWL5EH6qbBr127tm7bt99+y+7du5kyZUq9RB5g6NChTJgwgQULFjSp8nz//v2ZNGkSPp+PTp06cfPNNx/zOZqTRuZFRDqKox0BqtyDa/NXOPasBCDYZTD+4VeDu3UrsNZq0hp/EZF2wOqUjpXUFzM/B9vVo/6NVtvGqCrCSs0KVaD/ocgEfOPuxLnhc5zrPsaxcxFm8ZZQsbyEnq33IqTVbCtZz47SjSRFpjSYrm4YBkmRKWwv3cC2kvX0STr8yHdr2LVrV127NqfTSVJSEueccw5XX3016emNXNMHyc3N5fXXX2fZsmUUFBTg8/nq7S8sLKxb117r4CrytVJSUgCorKys27Z69Wog1DLuxRdfbPCYoqIiLMtix44dR5xB8ENbtmxh7ty5dedZvXo1I0eOPKZzNCcl8yIiHcXBI0DuqIb7AzVg27hXvhMa+TYc+Af+lGCf08M2dbNJa/xFRNoLw8Q/bDKeuTMwSndiRyeHbqwGajCqiiAiDv/QyYeeEWWYBDLPJ9ipH+6lr2BUFeCZOwP/wEsJ9jlD0+47mEpfKf6gF48zotH9HkcEJcECKn2lrRvYIYwdO5YnnnjimB+3c+dObrjhBqqqqhg+fDjjx48nOjoawzBYvnw52dnZDZJ7oNG2bw6HAwi1iKtVXl4OwOeff37YOKqrq48p7kAgUFfN/pe//CVPPvkkf/zjH5k1axZRUY187moFSuZFRDqIw44AWRZmyXYwwLaC2DGp+0d3ehzbk9hW8/UzbsIafxGR9sZKG4Z34r0HZiDtKwrNQErNwj/06GYg2cnpeM/4b1zZM3Hs/h7XqvcwC9bhH3Y1eGJa4VVIa4hxJ+ByePAGaoh0NUxcvcEaXA4PMe6E1g+uGb399tuUl5fz0EMPcd5559Xb95e//IXs7GNozdiI2qT/0UcfZfz48cd1roO99tprrFu3jiuuuILLL7+cyspKnn/+eZ5++mnuvffeZnueY6FkXkSkozjUCJCvErN4K4ZtYSX3JdhzLP7BPwdX43f+D6W5p8Mfa5VnEZH2ykobhrfrkOO7GeqOxj/6Jqytc3Gteh/HnlWY//kj/pHXYXXS78iOoFdiJj0T+rGpaBVpzl71ptrbtk1xdT79kgfTK7HhdPP2JDc3F4AJEybU225ZFitXrjzu8w8YEGqru2rVqqNO5k3TxLIOXYtg/fr1vPbaa/To0aOuHsBVV13F3Llz+eCDD5g0aVKD9fytQUMdIiIdSO0IkJWSheGtwCjeglm0GRxugin98Z16J/4R1zYpkffMnYGZvxbbE4cd1w3bE1c3Hd7Ma8Jd9KNZ4x/0HajyLCLSnhkmVucMrO6jQjcomzLjyDAI9jkd78T7sGNSMWpKcX/7BM51n6hwaAdgGiY/ybqeGHc8eRXbqPZXYVlBqv1V5FVsI9adwI+zrmsX/eYPp0uXUKvF77//vt72mTNnsnnz5uM+/2mnnUaXLl14++23Gx3lDwQCrFixot62uLg48vPzGz2f3+9n+vTpWJbFb3/7WyIiQp+hnE4nDz74IE6nkz/+8Y9NKqh3vDQyLyLSwVhpw/CmDsC15GWcW+dhx6YR7JyBf9Q07NjUYz9hS02HP5o1/o1UeRYROdHZCT3wnv4ArpVv49ixEOe6jzEL1uMbeT1EJoQ7PDkOA1JHcfPoh+r6zJcEC3A5PPRLHsyPs65r023pjtYll1zCxx9/zAMPPMCZZ55JfHw8q1evZsOGDYwbN4758+cf1/ndbjd/+tOf+H//7/9xyy23MHLkSPr27QvAnj17+P7774mPj+edd96pe8yIESP46quveOCBB8jIyMDhcHDqqaeSnp7OCy+8wObNm5k8eTKDBw+u91x9+vRh6tSpPPvss8yYMYO77777uGI/VkrmRUQ6Gl8V7sUvYhaux45MINB7IoGBl4YS4yZoqenwx1XlWUTkROf04B9+DVan/ri+fwuzaCOeuX/BN+Zm7MSTwh2dHIcBqaPIShnBtpL1VPpKiXEn0Csxs92PyNfKzMzkr3/9K88//zxff/01DoeDQYMG8fzzz/PNN98cdzIPcPLJJzNz5kxmzZrFd999x8qVK3G5XHTu3JnTTjuNc845p97x99xzDwDLli1j7ty5WJZFUlISNTU1vPnmm/Tq1Ysbb7yx0eeaMmUKc+fO5Z133mHcuHGMGtV6N1wM27btVnu2NqqkpKRZz5eYmNjs5xQJF13P7YtRsRv3wmcxqgrA4cE34prjrgZv7lqC58uHsOO6geloeIAVxCjPxXvWQ1jdj+0P2IFq9uWNVnluzmr2upalo9C1LD9kVOzFvehZjMq9YLpCv/u7jQh3WEeka1k6kua+nhMTE494TMe4vSMiIph7VuOeOwOjqgA7KhnvxF83TyJ88HT4xhzHdPgGa/zLczG8FVipWWpLJyJylOzY1P2/SweA5ce95CWcOR+BxuxEOjRNsxcRae9sG8emL3Gt+QCwsZL74Rt9Y7O1K2rp6fDNUuVZRORE54rCd8qtONd8gHPTlzjXf4pRsRv/8GsOXWhURNo1JfMiIu1Z0I/r+zdx7FgY+vKkcfiH/BzMZvz1fqiWdwdNh/cPnXx8yff+Ks8iInIcDJPAwJ9ix3bFteJNHHnZGFWF+MbcDFFJ4Y5ORJqZhj1ERNorbwXu+U/sT+QN/IN+FkqqmzOR30/T4UVE2o/gSafiHf//sN2xmGU78Xz9Z4yi42/5JSJti0bmRUTaIaNsV6jQXXUxtisS/6ipWCknt+hzajq8iEj7YSf3xXf6fbgWPodZvgvPt/+Lf9hkgj1PCXdoItJMlMyLiLQzZt4K3Mteg6AXOyYV35hbmtY/vik0HV5EpN2wo5LxnfYrXMtew7F7Ba7lb2CU5xEYcIluxIp0APopFhFpL2wb5/p/4V78PAS9WJ374z3t162XyIuISPvj9OAffSOBzAtCX276EtfCZ8FfHebAROR4KZkXEWkPgj5cS1/BmfNPAAJ9zsB3yu3gjg5zYCIi0uYZBoGsH+EbeT2YLhx7V+OZNwOjMj/ckYnIcVAyLyLS1lWX4v7mcRy5S8Fw4B/yCwKDfwamI9yRiYhIO2J1H4V3wj3YEfEYFXtwz52BWbA+3GGJSBMpmRcRacOMkm145v4Zs3Q7tisa36l3Euw9IdxhiYhIO2Un9sI78X6shJMw/FW4v3sKx9ZvDnGwhVmwAXPXEsyCDWBbrRusiByWCuCJiLRR5q4luJfPBMuPHdsF39hbsaM7hzssERFp7yIT8E24B1f2/+HYtQTX929ilOcSGHRZXXtTMy8bV/YszOLNEPSBw42V1Bf/sMlqRyrSRmhkXkSkrbFtnGv/iXvpK2D5CaYOxHvavUrkRUSk+Tjc+Edch//kHwPg3DoX94KnwVeFmZeNZ+4MzPy12J447Lhu2J44zPyc0Pa87DAHLyKgkXkRkbYl4MW1/HUc+z8oBdLPJjDgJ2ohJCIizc8wCGachx3bFffSVzEL1uP5+s+wrxhqyrATeoJhhI51R2G7emCU7sS1YhberkP0t0kkzPQTKCLSRhj7inDPezSUyBsO/MOvJjDwUn1YEhGRFmV1HRJqdRqZhFmyHUfuEmxX1IFEvpZhYEcnYxZtxizcFJ5gRaSOPiGKiLQBRtFm3F//BbN8F7Y7Fu/4/0ew5ynhDktERE4Qdnw3vKffjxWTgmEFMSt2Y1QVgm3XP9AZEVpD7y0LT6AiUkfT7EVEjsS2QiMQ3jLwxGN1Sm/W0XLHjgW4smeBHcSK645v7C0QldRs5xcRETkqnlj8I2/AsWsJBH0YFbshUIMdl3bg716gBhxu8MSHN1YRUTIvInI4LVrN17Zxrv0Q58bPAQimDcM//BpwepohchERkWNnpWQS7DYSM3cZhh3AqC7BCPqwEk4Cw8SoKsJKzQrd2BaRsNI0exGRQ2jRar5WEFf2G3WJfCDzAvyjpimRFxGR8DJM/MOnQFxXbEdEqLe8txKzcD1G8TaIiMM/dLLquYi0AfopFBFpjG2Fpr7XVvN1R4HpCFXzTegBNeW4VswKfcg5VkEfrsUv4NixcP+HpqsJZP2oYaEhERGRMLDShuGdeC9W2lDsyMTQlHtfFQYW3tE3qs+8SBuhafYiIo0wCzdhFm8O9XY/QjVfq3PG0Z/Yvw/3wucwizaC6cQ3aipW1yHNG7yIiMhxstKG4e06JFQzpnQ77pyPQjejN3yGr1MGdny3cIcocsLTyLyISGO8ZaE18oea9t6Uar415bi/fQKzaCO2MwLfqXcokRcRkbbLMLE6Z2D1O5uas6djxXfH8Jbj/vYxjCK1phMJNyXzIiKN8cSHqvUGvI3vP8ZqvkZVIZ5vHsUs24ntjsU3/h6sTscwoi8iIhJOkQn4JtyDldQXw1+NZ/6TmHtWhTsqkROaknkRkUZYndJDH1ga67Fr26Fqvsl9j6qar1Gei/ubRzGqCrAjk/Cd9qvQunsREZH2xBWF79Q7CaYOBMuPe9FzOHYsCndUIicsJfMiIo0xTPzDJkNEHEbpTvBVgRUEX1Xo66Os5msUbcb9zWMYNWVYsWl4T/s1dkxKK70IERGRZuZ04x9zE8EeY0LFYpe/hmPTV+GOSuSEpAJ4IiKHUFvNt67P/L6iUJ/51Cz8Q4/cZ97cuwb3oufB8mMl9cE39lZwRx84wLZChYW8ZeCJD43yq9WPiIi0daYT//BrsN3RODfPwbX6fQxfJYGsi9WZRaQVKZkXETmMetV8jyHpNnctwb3sdbCDWCkD8I2u30PezMs+cJMg6AvdJEjqi3/YkW8SiIiIhJ1hEBh4GbYnFtfaD3Fu+AzDV4l/yJW6MS3SSpTMi4gcyf5qvkfLseU/uFa+B9gEu4/CP/xqMA/8ujXzsvHMnRHqYR/dOZTkB7yY+Tl45s7Y39tXCb2IiLRxhkEw4zxwx+Ba8SaObd+Crwr/iOvA4Qp3dCIdnm6biYg0F9vGmfMRrpXvAjaBPqeHPtAclMhjW7iyZ4US+YSe4I4C0wHuqFBRvJpyXCtmgW2F7WWIiIgci2Cv8aEZaIYDR1427gXPgL8m3GGJdHhK5kVEmoNt41z5Ns71nwIQ6H8RgUE/a7B20CzchFm8OTQi/8N1hYaBHZ2MWbQ5NK1fRESknbDShuE79Q5weDAL1+Oe/wR4K8IdlkiHpmReROR4WQFcS1/BuXUeYOAf/HMC/S9svAiQtyy0Rv6g9fP1OCNC+71lLRqyiIhIc7M6Z+Idfze2OwazdDuebx6DfcXhDkukw1IyLyJyPAJe3AufxZG7FAwHvpHXE+wz8dDHe+LB4YaA9xDnqwnt98S3TLwiIiItyE7shW/Cr7AjEzEq9+KZ9z8YFbvDHZZIh6RkXkSkqXxVuOf/FTN/LTjc+MbegtV95GEfYnVKx0rqi1FVCLZdf6dtY1QVYSX3DVXMFxERaYfs2FS8p/0aO7YLRk0p7nmPYRRvDXdYIh2OknkRkaaoLsHzzaOYJVuxXdF4x92NlTrgyI8zTPzDJkNEHEbpTvBVgRUEX1Xo64g4/EMnq62PiIi0b5GJeCf8CiuxN4a/Ck/tzW8RaTb6tCgicoyMir145j2KUbEHOyIB32m/xE7qfdSPt9KGhdrPpWRheCswynMxvBVYqVlqSyciIh2HOxrfuLuwOmdB0It7wd8wdy0Nd1QiHYb6zIuIHAOjZDvuBc9g+CqwY1LxnXoHdlTyMZ/HShuGt+uQUNV6bxl44kNT6zUiLyIiHYnTg++UW3Etew1H7jLcS1/B76s6fH0ZETkqSuZFRI6SWbAe16LnMAI1WPE9Qi14PLFNP6FhYnXOaL4ARURE2iLTiX/k9djuaJxb5+Fa+TaGrxJ77ORwRybSrimZFxE5CmZeNu4lL4MdxOqUiW/MTeCKDHdYIiIi7YNhEhj8c3DH4Fz/Kc51H2M5Leh7UeOtXEXkiDSfU0TkCMxdS3AvfhHsIMGuQ/GdcqsSeRERkWNlGASyfoR/0M8AsNf/G9eKNxt2dxGRo6KReRGRwzBzl+Fe+ipgE+x5SqjSvOkId1giIiLtVrDvGeCKwrXqTRzbvwUD/EN+oRF6kWOkkXkRkUMwc5fjXvoKdYn8sKuUyIuIiBwv28KOTMToMx58VTi2foNz5dsaoRc5Ru1mZP7FF1/k0UcfBeCdd95h6NChDY6prKzkqaee4osvvqCgoIDOnTtzzjnncMcddxATE9PKEYtIe2bmZYcSedsi2GMM/mFTNGIgIiJynMy8bFzZszCLN2MRxPDVYAS8uLzloXX1g36mv7ciR6ldjMxv3ryZJ598kqioqEMes2/fPqZMmcJrr71G7969ufbaa+nbty+vvfYaU6ZMYd++fa0YsYi0Z+bu7+uK3QV7jME//Gq1jBMRETlOZl42nrkzMPPXYnviIKEHdmxXbMPELNqMa+2HOFe9pxF6kaPU5j+dBoNB7rvvPvr3789ZZ511yONeeuklcnJymDp1Kq+88gq/+tWveOmll7jtttvIycnhpZdeasWoRaS9MnevPFDsrttIJfIiIiLNwbZwZc+CmjLshJ7gjsIwHeCOwu6cieWKwijLxbl5Ds7Vf1dCL3IU2vwn1BdffJF169bxpz/9CYej8bWqtm3z3nvvERUVxW233VZv30033UR8fDzvv/8+tn4piMhhmHtW4178wv5EfgT+EdcqkRcREWkGZuEmzOLN2NGdG06jNwzshB7YTjeGbx/OzV/hXPMPJfQiR9CmP6Vu2LCBp59+mltuuYV+/fod8rht27aRn5/P8OHDG0zF93g8jBw5kr1797J9+/aWDllE2ilz7xrci54LJfJpw/GPuE7F7kRERJqLtwyCPnB6Gt/vjACHG3+f00JfbvoS59oPldCLHEabTeYDgQD3338/ffv25cYbbzzssbVJeq9evRrdf9JJJ9U7TkTkYGZ+zkGJ/DD8I5XIi4iINCtPPDjcEPA2vj9QAw43Vq/T8A++AgDnxs9x5vxTCb3IIbTZavbPPfcc69ev591338Xlch322IqKCoBDVqyv3V573A/Fx8djms17XyMxMbFZzycSTh35erZ3ryG4/GVwGBgnjcGccAeGo83+apTj1JGvZTmx6FqW9saOH0WwSxb27lUQFYexf6q9y+UKLYUtL8FIG4wnfRSGaWJFR2MtnYlr61eYMbGYQ34a5lcgcmSt/bu5TX5iXbduHc899xzXX389AwYMaPHnKysra9bzJSYmUlJS0qznFAmXjnw9mwXrcS94Biw/wS6D8A+aAuWN3/ST9q8jX8tyYtG1LO2VOeByPIVboWALdnQyzshYAtUVGFVFEBGH9+TLsGo/l6eOwpFZjmv1+7D8PQL7qgn0vzC8L0DkMJr7d/PR3Bhok9Ps77vvPnr06MEdd9xxVMfHxsYCoT7zjandXnuciIhZuAH3wr+FEvnUgfhHTQOzTd7fFBER6RCstGF4J96LlZKF4a2A0p0Y3gqs1KzQ9rRh9Y4Ppp+Jf8ClADjXfYxz3SfhCFukzWqTn1zXrVsHwKBBgxrdf8UVoXU0zzzzDGeddVbdmvht27Y1enztWvna40TkxGYWbsS94G8Q9GGlDMA/eho4Dr+cR0RERI6flTYMb9chmIWbiHVb7POZWJ3SD9k9JtjvbLBtXGs/wLnuYzBMApnnt3LUIm1Tm0zmL7vsska3L126lG3btjFp0iSSkpLo1q0bECp8l5KSwvLly9m3b1+9ivZer5elS5eSkpKiZF5EMIo2h6bWB71YnbPwjbkxVJBHREREWodhYnXOwExMxDqKacnBjHMAC9faD3Hm/BPbMAlmnNvycYq0cW0ymf/jH//Y6Pb777+fbdu2cdNNNzF06NC67YZhcPnll/PMM8/wzDPP8Otf/7pu3/PPP09ZWRm33XZbXaENETkxGcVbcS94en8i3x/f2JuVyIuIiLQDwYzzMGwbZ84/ca2dDYZBsN854Q5LJKzaZDLfFFOnTmXOnDm89NJL5OTkMGDAANatW8e8efPIyspi6tSp4Q5RRMLIKNmK+7snMQI1WJ0y8Y25RYm8iIhIOxLIPB9sC+e6j3Gt+QAMk2D6WeEOSyRs2mQBvKaIiopi5syZXHvttWzZsoVXX32VjRs3cu211zJz5sx6U+9F5MRilGzD/d1T+xP5DHxjbwGnEnkREZH2JtD/QgKZFwDgWv13HJu+CnNEIuFj2LZthzuIcGvu9i5qGSMdSXu/no3SHbjnP4Hhr8ZKTsd3yu3g9IQ7LAmD9n4ti9TStSwdRZOv5f3T7Z0bPgPAP+hnBPue0czRiRwbtaYTEWlGRulO3PP/Gkrkk/ookRcREekIDINA1sUE+oWK4LlWvYtjy9wwByXS+pTMi0iHZJTt2p/I78NK7IPvlDuUyIuIiHQUhkHg5B8T2F8Ez7XybRxb54U5KJHWpWReRDocoywX97dPYPirsBJ74zv1dnBFhDssERERaU6GQeDknxDYXwTP9f1bOLZ+E+agRFqPknkR6VCM8rz9I/JVWAkn7U/kI8MdloiIiLQEwyAw4FICfScB4Pr+TRzb54c5KJHWoWReRDoMo2J3aETeV4GV0BPfuDvBpU4WIiIiHZphEBh4GYE+oSJ4ruxZOHYsCHNQIi1PybyIdAzVJaH2c74KrPge+E5VIi8iInLCMAwCgy4n0HsiYOPK/j/MPavCHZVIi1IyLyLtn68Kz3dPYlSXYMd2wTfuLnBHhzsqERERaU2GQWDwFQR7jAHbwr34RYyizeGOSqTFKJkXkfYt4MW94BmM8t1gmPh7TcAsywXbCndkIiIi0toMA/+wKQRTB4Llx73wbxgVu8MdlUiLUDIvIu2XFcC15CUcu1dgFm7EKNmO55vH8Hx2H55P78XMyw53hCIiItLaTCf+UVOxEntj+Pfh/u4pqC4Jd1Qizc7ZlAcVFBTwzTffsHTpUtatW0dJSQkVFRXExsaSmJhIVlYWI0eOZNy4caSkpDR3zCIiYNu4VszCuX0+ZvEWLHcMdlRyqJd8wIuZn4Nn7gy8E+/FShsW7mhFRESkNTk9+E65Dc+8/8Go3IvnuyfxTviVluFJh2LYtm0f7cHffPMNb731FvPmzSMYDHK4hxqGgcPhYOLEiVx55ZWMHz++WQJuCSUlzXunLjExsdnPKRIubfV6dq6ZjXPDZ5gF67ANB3anfmAYBw6wbYzSnVipWXjPnwGGJiKd6NrqtSxyrHQtS0fRKtfyvuJQQl9TipXYJ1RXx+lu2eeUE1JzX8+JiYlHPOaoRuZXrVrFjBkzWLp0KQAjRoxg9OjRDB48mD59+hAfH09MTAwVFRWUl5ezefNmVq5cyaJFi5gzZw5z5sxh5MiR3HfffQwcOPD4XpWInPAcm77CufFzDN8+bIcbO7Zr/UQewDCwo5MxizZjFm7C6pwRnmBFREQkfKKS8J16B+5vHsUs2YJryYv4x9wEZpMmKIu0KUc1Mt+/f386derE1VdfzcUXX0yXLl2O+gl2797NP//5T2bOnElRURE5OTnHFXBL0Mi8yKG1tevZ3LUE99JXAAimDsS5/lPsuG5gOhoebAUxynPxnvUQVvdRrRyptDVt7VoWaSpdy9JRtOa1bBRtxjP/r2D5CfYci3/Y1Q0HAkSOQ5sdmb///vu58sor8Xg8xxxE165duemmm7j22mt5++23j/nxIiK1zPy1uJe9DkCg7ySs1ME4N30JAS+4G+kpH6gBhxs88a0cqYiIiLQldnJffKOm4l78PI4dC7E98QQG/CTcYYkcl6NaRHrttdc2KZE/mMfj4Zprrjmuc4jIicso2YZ70QtgBwl2G0lg4GVYnfthJfXFqCqEH04ysm2MqiKs5L5YndLDE7SIiIi0GVbXwfiH/gIA58bPcWz6KswRiRwfVYQSkTbPqNiLe8EzEPRide6Pf8Q1oalxhol/2GSIiMMo3Qm+KrCC4KsKfR0Rh3/oZBW/ExEREQCCJ43Df/KPAXCtfh/HzsVhjkik6Zq18kMgEOD9999nw4YNpKWlccUVVxAbG9ucTyEiJ5rqUtzfPYnhq8RK6IlvdP2iNVbaMLwT78WVPQuzeDPsKwKHGys1C//QyWpLJyIiIvUE+52LUVOOc8t/cC1/A9sTg5VycrjDEjlmTUrmn376aZ555hlef/11Ro8eDYBt21x77bUsW7YM27YxDIP33nuPv//978TExDRr0CJygvDvw73gaYzqYuzozvhOuR1cEQ0Os9KG4e06BLNwE3jLwBMfmlqvEXkRERH5IcMgMOhyDG8FjtyluBe9gHf83diJvcIdmcgxadIn3e+++47U1NS6RB7giy++YOnSpWRkZDB9+nTOPvtstm/fzqxZs5otWBE5gQR9uBc+h1mei+2Jw3fqneA5zEwfw8TqnIHVfVSoDZ0SeRERETkUw8A/4hqszv0h6MW94BmMir3hjkrkmDTp0+7OnTvp27dvvW2ff/45hmHw+OOP87Of/Yy//vWvpKWl8dlnnzVLoCJyArEtXMtewyzaiO2MwHfK7djRncIdlYiIiHQkphPf6JuwEnpi+Cpxf/ckVJeGOyqRo9akZL60tLRB37ulS5fSq1evuiTfMAwGDhxIXl7e8UcpIicO28b5/ds48rLBcOAfcwt2Qo9wRyUiIiIdkat20KAzRnUx7gVPg39fuKMSOSpNSuaTkpIoKSmp+3rnzp3k5+czatSoese5XC58Pt/xRSgiHYttYRZswNy1BLNgA9hWvd3O9Z/i3PYNYOAbdUNoyryIiIhIS/HE4jv1TmxPHGZ5Lu6Fz0FQOYy0fU0qgJeens7SpUvZvXs3Xbt25d1338UwDCZOnFjvuNzcXFJSUpolUBFp/8y87ANV54O+UNX5pL74h4Wqzju2foNz3ccA+AdfoUr0IiIi0irs6E74Trkd97ePYxZtxLX0Vfyjp6kGj7RpTbo6r732WrxeLxdffDGXXHIJL774It27d2fChAl1x1RUVLBmzRoyMzObLVgRab/MvGw8c2dg5q/F9sRhx3UL3QHPz8EzdwbOle/g+v4tAAKZFxDsM/EIZxQRERFpPnZCD/xjbgHDgWP3itDnEtsOd1gih9SkZH7ChAn87ne/IzY2lq1btzJixAiefvpp3G533TGzZ8/G7/dzyimnNFuwItJO2Rau7FlQU4ad0BPcUWA6wB0VWg9fVYh74bNgWwR7jSfQ/6JwRywiIiInIKtzBr5RNwAGjm3f1s0YFGmLDNtumdtNNTU1+P1+oqKicDgcLfEUzebg9f/NITExsdnPKRIuzXE9mwUb8Hx2H7YnLpTIH8xfjVm4ASPow3/yJfjOeEBT2qRF6HezdBS6lqWjaMvXsmPrvLoZg/7BP9eMQTmi5r6ef1hwvjFN+sR8++238/DDDx/2mIiICGJjY9t8Ii8ircBbFloj7/TU3x7wYZZsAwxs00Wg7yQl8iIiIhJ2wd6nEci8EADXyncwc5eHOSKRhpr0qXnu3LmUlpY2cygi0mF54sHhhoD3wDYrgFmyFawAmE7smBSISg5fjCIiIiIHCfS/kGCv8YCNe+krmAXrwx2SSD1NSua7d+9OdXV1c8ciIh2U1SkdK6kvRlVhqJCMFQyNyAd9YLqwnZFYnfphdUoPd6giIiIiIYaBf8iVBNOGgR3Eteg5jNKd4Y5KpE6TkvkLL7yQxYsXU1BQ0NzxiEhHZJj4h02GiDiMkh2YRZvBtw9sG9vhhqhE/EMna4q9iIiItC2GiX/EtVjJ/TACNbgXPIWxryjcUYkATUzmb7rpJkaOHMmUKVP497//jd/vb+64RKSDsdKG4Z14L7ijMbxlGAEvdkQCVtdBeCfeq57yIiIi0jY53PjG3owV1w3DW4Fr4bP1lw6KhImzKQ8677zzsG2b3bt3c+edd2IYBklJSXg8ngbHGobBl19+edyBikj7Z9SUYUcnE3RHEsi8ACtteGhqvUbkRUREpC1zReEbeyuer/+MWZ6La+kr+MfcDIYR7sjkBNakZD43N7fe17ZtU1hY2CwBiUjHZOavxbXqvdD6s2FTCPY7J9whiYiIiBy9qCR8Y2/G8+3/4tizEnvthwQG/CTcUckJrEnJ/Lp165o7DhHpwIyKPbiWvAS2RbDnWILpZ4c7JBEREemIbAuzcFOoLa4nvtlnANpJffAPuwrXsldxbvwcOzaVYM9Tmu38IseiScm8iMhR81XhXvg3DH81VlJf/EN+oSlpIiIi0uzMvGxc2bMwizeHOuY43KHPHsMmN2ttnmCP0RgVe3Bu+Beu7FlY0Z2xk9WRR1qfFqqKSMuxArgXv4BRVYAdmYRvzE3gcIU7KhEREelgzLxsPHNnYOavxfbEYcd1w/bEYebnhLbnZTfr8wWyflTXss696PlQ+12RVnZcyfzcuXO59dZbmTBhAgMHDuS//uu/6u175JFH2Lt373EHKSLtkG3j+v4dzMIN2M4IfKfcCp7YcEclIiIiHY1t4cqeBTVl2Ak9wR0FpgPcUdgJPaCmHNeKWWBbzfechoF/+DVY8T0wfJW4Fv4N/NXNd36Ro9DkZP6hhx7i5ptvZs6cOezbt49AIIBt23X74+LieP311/n000+bJVARaV8cm+fg2P4tYOAfeQN2XLdwh9Rklm2xpTiHlXsWsKU4B6s5PwyIiIjIcTELN2EWb8aO7txwKZ9hYEcnYxZtDq2lbyrbwizYgLlrCWbBhtCNAacH39hbsCPiMSt241r6SvPeMBA5giatmX///fd5++23GTJkCA8//DD9+/enf//+9Y4ZNmwYqampzJkzh+uuu65ZghWR9sHcsxrX6r8D4B/4U6wuA8McUdOt2buE2TmvsKN0I/6gF5fDQ8+Efvwk63oGpI4Kd3iHZNkW20rWU+krJcadQK/ETEy1ABQRkY7IWxZaI+9s2CYbAGcE7CsKHdcER1qL7xtzM55vHsexdzX26r8TGHT5cbwYkaPXpGT+nXfeIT4+nueee47ExMRDHtezZ0927drV5OBEpP0xyvNwLX0ZsAmeNI5g30nhDqnJ1uxdwnOLH6LSV0ZSZAoeZwTeQA2bilbx3OKHuHn0Q20yoW+vNyBERESaxBMPDjcEvKEp9j8UqAnt98Qf86lr1+JTUxYa+Xd6IOCtW4vvnXhvKKEfcQ3uJS/h3DwHO7YrwV7jm+GFiRxek4ZpNm3axLBhww6byAN07tyZoqKiJgUmIu2QtyJUuT5Qg5XcD/+Qn7fbyvWWbTE75xUqfWWkxfYi0hWNaTiIdEWTFtuLSl8ZH+a82uam3NfegNhUtIoYdxxdYnsQ446ruwGxZu+ScIcoIiLSrKxO6VhJfUNF6A5a9guAbWNUFWEl9w21qTsWx7AW3+o2gkD/iwBwff92aCq+SAtrUjJvGAaWdeQPsPn5+URGRjblKUSkvQn6Q9Vc9xVhR3cOVa4322/3y20l69lRupGkyBSMH9yQMAyDpMgUtpduYFvJ+jBF2FB7vQEhIiJyXAwT/7DJEBGHUboTfFVgBcFXFfo6Ig7/0MnH3G/+WNfiBzIvINhtJNhBXItfwKjMb65XKNKoJiXzffr0YfXq1VRXH7piY0lJCTk5OWRmZjY5OBFpJ2wb1/dvhf7gOSPwjb0F3NHhjuq4VPpK8Qe9eJwRje73OCLwB71U+kpbN7DDaI83IERERJqDlTYsNOU9JQvDW4FRnovhrcBKzaqbCn/MjmYtftB3YC2+YeAffhVWYi8MfxXuhX8D/76mvyiRI2hSMv+jH/2I4uJipk+fTiAQaLDftm3+8Ic/sG/fPn784x8fd5Ai0rY5Nn2JY8cCwMA/aip2bNdwh3TcYtwJuBwevIGaRvd7gzW4HB5i3AmtG9hhtMcbECIiIs3FShuG94IZeM/7C96zHgr9//wZTUvkof5a/MY0thbf4cY3+ibsyESMyr24F78YmiUg0gKalMz/4he/YMSIEXzwwQdccMEFTJ8+HYD169fzl7/8hfPPP59PPvmEMWPGcMkllzRrwCLStpi7V+Ja8wEA/kGXY6UOCHNEzaNXYiY9E/pRXJ1fr+0mhG5YFlfnc1JCBr0S287so/Z4A0JERKRZGSZW5wys7qOwOmcc89T6gzV5LX5kAr4xt4DDjVmwDufq95scg8jhNOnqdrlcvPTSS/z85z8nLy+PN998E4C1a9fy6quvsnPnTi677DKee+45TFOtkEQ6KqMsF/fSVwCbQK8JBPucHu6Qmo1pmPwk63pi3PHkVWyj2l+FZQWp9leRV7GNWHcCP866rk21e2uPNyDaM8u22FKcw8o9C9hSnKNaBCIiHc1xrMW3E3rgG3EtAM4tX+PYMreVg5cTgWH/8BPfMSouLmbx4sXk5uYSDAbp0qULY8aMITU1tblibHElJSXNer7ExMRmP6dIuBzyeq4pxzP3LxjVxVidMvGdenu7Lnh3KI21eTspIYMfZ13XJtu8NWin54jAG6yhuDqfWHcCN43+XZuMuzU05+9mtf+TcNLnDOko2su13Gif+eS++IdOPuIUfseGz3Ct/RAME98pt2OlZLVS1NLamvt6PlLnOGiGZL4jUDIvcmiNXs9BH+75f8Us3oIdnYJ34r3tvuDd4Vi2xbaS9VT6SolxJ9ArMbNNjcj/UHu7AdFamut3c4MbJs4IvIHQDZMYdzw3j37ohH6fpeXpc4Z0FO3qWratUNV6bxl44kNT64/ms4Bt41r+Oo6di7BdkfhOuxc7tkvLxyutLhzJfJOG0c4880zOO+88fv3rXx/2uMcee4x//etffPnll015GhFpi2wb14o3Q4m8KxLf2Fs7dCIPoSn3fZLaz530AamjyEoZ0a5uQLQXP2z/V9s1INIVTZqzF3kV2/gw51WyUkbo/RYR6Uj2r8U/9scZ+IdOxqgqwCzegnvh3/BOvK/Df3aS1tGkZD43N5fi4uIjHldSUkJubm5TnkJE2ijHxi9w7FwUWkc2ahp2bPtZUtOR+YM+ymqKKKkuoKSmkNLqQsq9JRiGgcNwsDZ/CQ7TicNw7P+/E6fpCv17/9cO03HQdseB/fv3OU1X3b+jXLG4HO5wv+xWdyzt/9rTDSAREWlBDhe+MTfj+frPGFUFuBe/2GGXJ0rratErqLq6GqdTF6lIR2HmZeNaOxsA/6Cfad1XK/IFaiitKaKkpoDS6kJKawopqa79dxGVvrJWjcfAICkqldSYHqTGdCMlujtdYnuQFJmKw3S0aiyt6Wja/5UEC9T+T0RE6vPE4ht7K555/4NZuB7X9+/gH/oL+MGNYZFj0SKZtmVZbN26lUWLFpGWltYSTyEircwo3Yl72WsABPqcTrDPxPAG1MHUBPbVJeml1YWU1BRSUl1YN9q+z19xxHO4HR4SIzuTENGJhMhOxHuSMAyToBUgaAcIWgECVoCA5d//dZCg5SdoB0PbrOD+7X4C1oHjax9be56AFcCygxTt20PRvj2szV9SF4PDcNI5Jo0uMT1Iie5GSkx3usT0ID4iucFIdmuprXlgVAWxvY7jWnJwcPu/SFfDKZJq/yciIodix3fDN+oG3AufxbH9W6y4rgT7Tgp3WNKOHXUyn5VVfwRu9uzZzJ49+7CPsW2bn/3sZ00KTETakOpS3IuehaAPq3MWgYGXhTuids22bXZX7GBT8So2F60hr3wr1YGqIz4uwhlFQmSnULIe0YnEyM4k1n4d2YlIZ3SrJMy2bVPpK2Nv5S72Vu4iv3IXeyt3kl+Viy/oZU/FDvZU7Kj3GLcjgtT9iX1KTHdSY7qTGtODaHdsi8Z6cDFAiwAmzuOqOl/b/m9T0SrSnL3qvd+17f/6JQ9W+z8REWmU1WUQ/gGX4FrzD1yr3seOScVKHRDusKSdOupkvmvXrnX/3r17NxEREYessOdyuUhJSeHMM8/kqquuOv4oRSRs7IAP9+LnMapLsGNS8Y2eCh14GnVLKfeWsKloNZuKVrG5eA1VvvIGx0S6YkiM6ERCZGcSIpIPGmVPJiGiU6MjweFgGAaxngRiPQmkJw+s227bNiXVBfuT/J3srQol+gVVu/EFa9hZtomdZZvqnSvGHV+X3HeJ6UFqTA/S4no1S/G4H1adj4mIpbKmgk1Fq3hu8UNNqjpvGiY/ybqe5xY/RF7Ftkbb//046zoVvxMRkUMKpp+FWbEHx47vcC15Cd9pv8aO02xmOXZNak3Xv39/LrnkEh555JGWiKnVqTWdyCHYNnFr38K38RtsVzS+ifdix6SEO6p2wReoYVvp+roEPr+qfjFQt8ND78Qs0pMH0Ssxc3+Ls8gwRduyglaAwn17Qgn+/tH8vZW7KKnOb/T4GHc8WZ2Hc3LqKPokZuFoQoEgy7Z4ZO5toRH0/VXnXS4Xfr8f27bJq9hGv+TB3D/x6SYl3mr/J+GkzxnSUZzQ17IVwD3/ScyijdhRyaEK956Wna0mLavdtKZ744036NSpU1MeKiLtiHP9p9jbFoLhwD/6RiXyh1GbIG4uWs2motVsL91A0A7U7Tcw6BbXm77JA0lPHkjP+H5NSlLbI4fp3D+tvnu97b5ADflVefWS/NzyLVT6yliS+x+W5P6HCGcU/TsPY0DKaNKTBx51Bf2Wrjqv9n8iInJcTCe+0TfimfsXjH2FuBc9j2/cXeBwhTsyaUea9Ely9OjR9b72+XyUlpbidrtJSEhojrhEJMzMvGyc6z4Glwv/kCub1lu1gyurKTpo6vzaBkXqEiI6kZ48kPTkQfRJOpkoV0yYIm2b3M4Iusf3oXt8n7ptQSvAlpIc1u5dwtqCZVT5ylmxez4rds/H5XCTkTyUAakjyeg0hAhn1CHP3RpV503DVPs5ERFpOk8MvlNuxT13BmbxZlyr3sU/dHK4o5J25LiGhd58803eeecdNm7ciG3b/OQnP6mbev/pp5/y6aef8qtf/YpevXo1R6wi0kqMir24lr8BgNn/XIK9xoU5orbBF6hhS0lOKIEvXkVh1e56+92OCPoknVyXwCdHpoatgnt75TCd9EseRL/kQfzIvpYdpRtYm7+MNflLKKspYk3+YtbkL8ZhOElPHsjJKaPIShne4EaJqs6LiEh7YMd2xT9qKu4Fz+DY9i1WUh+CPU8Jd1jSTjQpmQ8EAtx+++3MnTsXl8tF37592bhxY71j+vbty5dffsnJJ5/Mrbfe2izBikgrCHhxL34OI1CDldwPY/iVUNawWNuJImgFWJu/lCW5X7O9ZH2DqfPd4/uQnjyI9KSBdI/ve8JMnW8NpmHSK7E/vRL7c37GL8gt38ra/KWszV9K4b7drC9cwfrCFczOMemd0L9u6nucJ1FV50VEpH2wLTBdBFP649y5FNeKN7HiumMn9Ah3ZNIONHnN/Ndff80ZZ5zBH/7wB5KTk+nfv3+9YzIzM+nevTvz5s1TMi/SXtg2ruz/w6jYgx0Rj2/UDUSfoJXry70lLNn1H5bm/ocKb2nd9sTIzqQnHZg631YqzHd0hmHUTck/O/1y8qty6xL73RXb2VKyli0la/lo3ev0iE9nQMooJvW+hD0VO+qqzpuOWKr9Vao6LyIibYKZl40rexZm8WYIeDG85RilDjxfP0LNBf8Dbn3GkMNrUjI/e/ZsOnfuzP/+7/8SEdH4ekSAHj16sHnz5iYHJyKty7Hlaxy5S8Fw4Bs1FSLiwx1Sq7Jtm+2l61m489+szV+GZQeBUHX1Ud0nMbTrOJKjUpvt+SzbUgG1JjAMo66g3hl9fkLxvnzW5i9lTf6SuvZ3tS3wOkenYRgmpdWFlHmLcBgu+iUPVtV5EREJKzMvG8/cGVBThh3dGaI92L59mIUbcOQuw/31n/GdPR20XE8Oo0nJ/Pbt25kwYcJhE3k4wdtNiLQzRtFmXKv/DoB/wCXYyelhjqj1+AI1rNjzHYt2fsneyp11209KyGBsj7M5OWVks0+fb6y1Wc+Efvwk63olmccoKSqF8b0uYHyvCyj3lpCTv4w1+UvZWpKDL1hDrCcep+EkJb4rw1NPZ1LfS3FqOYSIiISLbeHKnhVK5BN6HkjYI2KxUrJw7F2Nc+s8rPX/ItD/gvDGKm1akz7NuN1u9u3bd8Tj8vLyiI1Vv0SRNs9bgXvJi2AHCaYNJ9h3UrgjahWF+/aweOdXLM+bR00g9DvNaboZ2vVUxvQ4i66xJ7XI867Zu4TnFj9Epa9sf3/5CLyBGjYVreK5xQ9x8+iHlNA3UZwnkTE9zmJMj7Oo8lWwriCbtflL2FS0Gm+ghnnbPmJN/hIm9LqQoV3H4TTVAkhERFqXWbgJs3hzaET+hyPv7iis+O6Y5bk4V76NldQbK0WdU6RxTUrmMzIyWLVqFSUlJYdsZp+Xl8fatWsZO3bscQUoIi3MCuJe8jJGTRl2TCr+YVd16Cldlm2xofB7Fu38NxuLVtVtT4pMZUyPsxieNqFF18FbtsXsnFeo9JWRFnugMFukK5o0Zy/yKrbxYc6rZKWMaJNT7tvT0oBodywjup3GiG6nUeWrYGXxfObkzKZo3x5mr32Zrzb/nXE9z2dU9zPwOCPDHa6IiJwovGUQ9IHT0+huOyYVe18RRtCPa8nLeM/4L4hKauUgpT1oUjJ/2WWX8cADD/DrX/+axx57jPj4+utqq6qq+M1vfoPf7+fyyy9vlkBFpGU4cz7CLFwPDg++MTeB6/DLZ9qrff5KluXOZfGuryipLgBC1egzOg1hTI+z6Jc8uFXayG0rWc+O0o0kRaY0eD7DMEiKTGF76Qa2laxvcz3M2/PSgGh3LBdkXcmITqezJPdr5m//lHJvCZ9tfIuvt37I2B5nM7bnOcS448IdqoiIdHSeeHC4IeAFd1TD/YEa7KhOWPE9MHwVuBe/gG/CL8Gh2WRSX5OS+UsuuYSvv/6azz//nDPPPJMRI0YAkJ2dzZ133smiRYsoKyvjoosu4uyzz27WgEWk+Zh5K3Bu/BwA3/CrsGO7hjmi5pdXvo2FO//Nyj0LCVg+ACKd0Qzvdhpjup9FUlRKq8ZT6SvFH/TicTZ+08TjiKAkWEClr7RV4zqSjrI0wO2MYNxJ5zGmx5l8v/s7vtn2CYX7dvP11g/5dvu/GNntdMaddB6JkZ3DHaqIiHRQVqd0rKS+mPk52K4e9WdE2jZGVRFWaha+036Fe+5fMEu341z1HoGhvwhf0NImNbkC0BNPPMFLL73EK6+8wty5cwHYtm0b27ZtIzY2lrvuuoubb7652QIVkeZlVObjWv46AIG+k7C6jQhzRM0nYPlZvXcxi3b+m51lBzpqdI09ibE9zmZQl7G4HY1PbWtpMe4EXA4P3kBNo9P5vcEaXA4PMe6E1g/uENr70oDGOE0XI7pNZFjaBHLylzFv28fklm9h4c4vWLTrS4Z0OYUJvS4iNaZ7uEMVEZGOxjDxD5uMZ+4MjNKd2NHJ4IyAQA1GVRFExOEfOhk7JgX/yOtxL3gG57ZvsJP6EOypJcxyQJOTecMwmDZtGtdffz1r164lNzcXy7JITU1l0KBBuN3u5oxTRJpTwItr8QsYgRqspL4EBlwS7oiaRaW3jAU7v2Bp7tdU+coBcBhOTk4dydgeZ9Mzvl+rTKU/nF6JmfRM6MemolWkOXvVi8e2bYqr8+mXPJheiZlhjLK+9rw04EhMw2RA6ihOThnJluK1zNv2MZuLV7Ni93xW7J5P/87DOa3XRfRM6BfuUEVEpAOx0obhnXjvgT7z+4rA4cZKzcI/dDJW2rDQcakDCGRegHP9J7hWvIkV3x07XjeaJeS4e/M4HA4GDRrEoEGDmiMeETkWtoVZuClUSMUTj9UpHY40MmrbuL5/C7M8F9sTG+on387bdPmDPhbs+Jyvt/4TX7AGgFhPAqO7n8nIbqcT60kIb4AHMQ2Tn2Rdz3OLHyKvYltoyrojAm+whuLqfGLdCfw467o2NcLdXpcGHAvDMOibPIC+yQPYVbaFeds+Jid/KesKlrOuYDm9EvtzWq+LWq22goiIdHxW2jC8XYcc8bNcoP+FmCXbMPPX4F78At7T7wdXI2vt5YTTvj/Bi5zAzLzsA3dzg77Q3dykvviHHbib2xjH1nk4di4KTfEaNRUiE1ov6GZm2zZr8pfw+ca364rapcX15rReF5HVeXiz94ZvLgNSR3Hz6IfqismVBAtwOTz0Sx7Mj7Oua3Nrz9vj0oDj0T2+D78YcieFVbv5ZtsnrNg9n20l69hWso4usT05rddFDEgZjcN0hDtUERFp7wwTq3PGEY4x8I28Ds9//oRRVYBr2Wv4x9zSobsPydExbNu2m/JAn8/HJ598wuLFiykoKMDn8zX+BIbB66+/flxBtrSSkpJmPV9iYmKzn1PkYGZeNp65M6CmLNSj1OmBgBfj/7N33+FVldnix7/vPi3lpDcSWigBAgkdbNiwV0DFBhawjqgz987Vqb8Z587cca5zvc510LE3xC5iw4YgdmqAACEEQkIJJCG9nrbf3x9bUEiAkHZS1ud5fI6cvc8+68DOzln7fd+16g5ASCSeM+9vNqFX5TtxffUw6AC+UVcQSDt+gcquej7vrd7JktyFFFbmAlbCObrPyfSNHESEK6ZLt0w7qLu0eTO1yYMr5llLAyKaLg0oqikgLW40vz5zfpeM/6DWnstVjeV8u+tjVu9ZhjfgsY4VmsjpqZcwLnkKDpssKxOdq6tel4U4UXIunxhVUYDry/8BHcCffjn+4RcFOyTxE+19Ph+tBfxPtSqZLyoqYs6cOezatYvjvVwpRU5Ozom+RaeSZF50K9rEteR+jJIt6OgBTSugVu7GTErHc9FDh0/T8tTg+uJBVEMFgZRx+Cbd1qI7ul3tfK72VPDZ9jdZX/Q1Go3dcDIoZgSFlXnsrc7vdi3Tuosm1eyPWBpwx+Q/dvm/67aey/W+WlbuXsq3uz6hwVcLgNsZxakDL+SU/udLUi86TVe7LgvRWnIunzhbwdc41i8EFN5T78FM7F61anqybpPMz5s3j88//5yTTz6ZG2+8kQEDBhAaGnrU/fv27Xuib9GpJJkX3YlRug3Xx79CuyKb703qrUN5avBc+N8/TtvSJs5v/4lRuhXtTsJz5q/AcfSf2Z9q7/O5taPR3oCHbwo/4quCDw6Njo7pcyoDotN4ef0jTVqmlTeU4HZGdZuWad1Bc33mB0YP65JLA5rTXuey19/I2qIv+brwQ6oaywGID0vmshE3MSRuVJuPL8TxyPcM0VPIudwKWuPIWoBt13dopxvP2b+F0OMnfaLjBSOZb9WC0u+++46BAwfyzDPPYLd3zTWpQvRYniprjbz9KK3V7CFWRVRP1Y9P5byPUboVbE68k29vcSLf3ppLBo83gq61Jrv4ez7Je+1Q4tQ/aggXD59N38jBPLhiXo9qmdaVjUqaRHrihG6xNKAjOe0hnDLgfCb3m8qG/d/xad7rHKjfx/Pr/saYPqdy0bDrcbuigh2mEEKInkgpfGOuRVXtwajajXPVU3in/DvYHMGOTARBqzJxh8PBiBEjOiyRr66u5tFHHyU7O5s9e/ZQVVVFTEwMgwYNYtasWZx//vlNqgnX1tbyz3/+k08//ZTS0lISEhI4//zzueeee3C73R0SpxBB4YoCmxP8nuZH5v2N1vYfkglj3wbs2z4GwDtuNjoypWXv80OlfLPKxPAaLauUfwxNpmn/MIK+vSybJ1Y90OwI+u6q7SzJfflQr/iokFguSLuWzKSTUUqRX57TY1umdVWGMuTv8gc2w874lNNJTxjP5zveZuXupWzY/y25B9Zz/tCrmdRvaqsr33eXegpCCCGCwObEN/k2nF88iFFRgH3TW/jHXBfsqEQQtCobHzt2LPn5+e0dyyEVFRW8/fbbjBkzhnPOOYfo6GjKyspYvnw59957L1dffTV//vOfD+1fX1/P7NmzycnJ4bTTTuOSSy5h69atvPDCC6xcuZJXXnmFsDBp3yB6BjN+KGbsEIySHLSjf9M183VlmEnpmPFDUbUlONZZBSj9g8/G7NeyqdA/rZQfIIALW4sq5R81Zm2yOOe5Fo+gVzWW8Une62zc/x0ATpuL01MvZcrAiw9bl9wbWqaJri/UEc6lI25kbPIU3st5nqKaAt7b+gJZ+77i8vQ5JEcMPKHjtWYGixBCiN5FhyfgmzAH5/ePY9/5JTpmMIEBJwU7LNHJWpXM33vvvcyePZsFCxZwww03tHdM9OvXj9WrVzcZ+a+treWaa67hjTfe4MYbbyQtLQ2AZ555hpycHG699Vbuu+++Q/s/+uijPPbYYzzzzDPce++97R6nEEGhDHzjZuFa8RCqcjc6PM6aWu9vRNWVQUgkvrGzIODHsepplK8BM3Yw/owrWnT4JpXyQ93ohlqMkhxcKx46aqX8YymoyG3RCHregWz2VG/nq4Il+E0vCsXYlCmcN3Qmka6m64Z6W8s00bX1ixrMnSf9iZW7l/LZ9jfZXbWDx1f+gVMHXMA5g6/AeZSbTj/VmhksQggheiezTyb+4Rdjz12CY/1CzKh+6KiuXatMtK9WJfOjRo3i2Wef5b777uPTTz/ltNNOIykp6ajTCadPn35Cx7fZmu/d63a7mTJlCtu3b6ewsJC0tDS01rz55puEhYUxb968w/a/4447ePnll3nrrbe45557Wj3dUYiuxkwZh+fM+3/sM19fZvWZT0rHN3YWZvJYHFkvYVTvQTsj8E66FVrSc12bOLIWWon8D5XylWEDZxja0R9VuRvH+oV4ksec0JT7442gOw0XNZ4Knl/3IHbDWvM1MHo4Fw+fRd/IQUc9bmrMcAZEp1kt0+xNW6aVN5SQFjea1JjhLY5ViLYwlMEpA85nVNIkluS+zKbiVXxT+BGbildyyfAbSE+YcNTfRSc6g0UIIYTwj7gEo3wnRmkOzlVP4jnr1+CQGcm9RasXvX/77beUl5ezd+9e1qxZ0+w+WmuUUieczB+Nx+Ph+++/RynF0KFDASgoKKCkpIQpU6Y0mUrvcrmYOHEin3/+OYWFhaSmprZLHEJ0BWbKODzJYzAObLeK3bmiDq1rt+38Ctuu7wGFb9ItLa5yahzYjlG+wxqRPzLhUAodHodRtsNaS3+wUn4LHGsEvd5XS1F1AfW+Wrx+DwlRKVyYdh0jEyce9wacoQymp8/liVUPUFRT0GzLtGnpcyTxEZ0u0hXDtaPvYduBDby/9UUqGkp5ZcP/MTx+LJeOuJGY0IQmr2npDBapASGEEOIQZeCddAuu5X9F1ZXiWPsivpPubFH7YdH9tSqZf+aZZ5g/fz5Op5Pzzz+ffv36dcia9Orqal588UVM06SsrIwvv/ySffv2cffddx9KzAsLCwGOmqgPHDjw0H6SzIseRxlNkmpVUYBj4+sA+EZOx0w4gVHpVlTKb4nmRtC9AQ+ldUXUeCpp9NcT6Yph2si5nDrgghPq1z0qaRJ3Tn7g0BrjikApDpuLtLjR3aZlmui5Bd+GxY/hnlMeZMXO9/i6YAm5B9aT/90Wzh48g9MGXIjtJzNmpAaEEEKIVnGG4518G64v/wfb/o2YeZ8SGHZBsKMSnaBVyfyrr76K2+3mjTfeYPDgwe0d0yHV1dXMnz//0J8dDgf3338/c+fOPfRcTU0NwFEr1h98/uB+zYmKisIw2vdLY0v6AgrR3nRjDYEvXgS7geo/mZDJV5/Q8hLt6Y/fGQYqgHL8mFA4HI4ftnvBGUZkQn/UCZ7jN0z+OY+s+BX7a3ehlKLaU0HA9OMNeIgNS+C3587nlEHnn9AxD5oScz6nDj+XHQe2UOOpIMIVw5D4kT0iGewNNuz9jtfX/4udZVsPFXwbFDeCa8b+jDF9T2nX9wrWtfnq+Ns5Y/hFvLnhSXYc2MzygkVsLV/D1WPvZFCcNcqe4h9AiDOMAH5cjqYJfb3XQ4gzjJT4AfI7Rsg5IHoMOZfbSUwMZuAWzJXP48hbgm1AJip5VLCj6nU6+3xuVTJ/4MABTj311A5N5MEqhJebm0sgEGDfvn0sWbKERx55hKysLP7xj3+0W2u8qqoTG2U8npiYGCoqKtr1mEIclzZxfjsfo3I/OjwRT/pVUFl5YsdwJuKKGmhVyo+2KuU7HA58Pp9VKb9qP2ZSOrXORDjBc3xA2AiuGvkznl79Z8obSjC1SYg9jLF9pnDt6LsZET2pzT838fa+xNutwi9Vle37cy06xpEF36JdcXj8jWzZt5a/l/2yXQu+Bfva7MLNrFH/Tta+r/lo2yvsKtvB/3x+HxP6nskFadcSa0+mb8RgawZLRNMaECU1RaTFjSbWliy/Y3q5YJ/LQrQXOZfbWdxYHMkTsO36Hu+yf+A5+zctXmop2q69z+eW3Bho1bDVgAEDqK+vb81LW8Vms9GvXz9uv/12fvGLX/DZZ5/xxhtvABAREQFYle6bc/D5g/sJ0VPZt36IUZoDhgPv5NtbV/zkh0r5hESiKneDtw5tBsBbZ/35YKX8Exzx1lqzZu8XLN3xJn2jBpGRNInbJv6O/77gdf507vMyFb6XOrLgW6gjHEPZrIJvEanUeqt4N+d5TG0GO9R2o5RifMrp/Ntpf2dCypkArN27gn98cz/r933DtBFzcDujKKopoMFXh2kGaPDVUVRTIDUghBBCHJtS+MZchxnZD+WtwbnqaTD9wY5KdKBWfSO49tpr2bBhAzt27GjveI5rypQpAKxatQr4cU18QUFBs/sfXFN/cD8heiJjfzb23CUA+MbNblNbkoOV8s3EdJSnBip3ozw1mEnprWpLV++r5dWN/2TxlmfxBbwMiRnFr86Yz7SRcxkSJ1Phe7MTKfjW04Q53MwYdSu3Tfw9ieF9qffVsGjzU3y/5zOuyZzH0LhMar3V7K/dTa23mrS40dwx+Y9y40sIIcSx2Zz4Jt+GdoRiVOzEnv1WsCMSHahV89RnzZrFrl27uOWWW/jFL37BKaecQlJSUnvH1qzi4mLgx/Z1qampJCYmsm7dOurr6w8rxOfxeFizZg2JiYmSzIseS9UdwLH2BQD8g84k0H9ym4/500r5EU6Teq9xqFL+icgvz+HtzU9Q1ViOoWycN3QmUwZeLG0iBSAF3wAGxgxn3sl/4ZtdH7NsxzsUVGxld+V2Tht4ETMzfkajv65HFQQUQgjR8bQ7Ed+EOTi/fxz7zhXo2MHt8v1QdD2tSubT061iPVprfvOb3xxzX6UUW7ZsOaHj5+Tk0K9fvyZT4ysrK3nkkUcAOOOMMw4df+bMmTz22GM89thj3HfffYf2f/LJJ6mqqmLevHmSPIieKeDDseoplK8eM2YQ/syr2u/YP1TKN2JiME9w/U/A9LMs/x2+3Pk+Gk1cWB+uzrzrmD3jRe9zrJaFAJ5AIw6bC7czuvOD60Q2w84ZqZeSmXQSH2x9idwD6/my4H2yi1cyY+St0oZOCCHECTP7ZOIfdiH2bR/jWP8KZvRAdETnDL6KztOqZD45Obm94zjMokWLeOuttzjppJNISUkhNDSUoqIivvjiC+rr67ngggu47LLLDu1/6623smzZMp555hlycnIYNWoUW7du5csvvyQ9PZ1bb721Q+MVIljsW97FqNqNdlgtSTDapyhkW5TVF/Nm9uPsqc4HYELKmVwyfDbOo4y+it6ruZaFB2mtKW8oIS1uNKkxJ9BesRuLCU1g9th/J6d0LR9sfYmKhhKeX/sg5w2dyempl8pNaSGEECfEn34ZRvlOjAO5ONY8i/eM+8DmCHZYoh0prbUOdhBHWrNmDW+//Tbr16+npKSExsZGoqKiGDlyJNOnT+eSSy5p8qWmpqaG+fPn88knn3DgwAHi4+O54IILuPvuu49b/K69q2hKZU7RGYzizTi/s1o3ek/6GWby6A55n5aez1prsvZ9zQdbX8Qb8BBiD2P6yFvISJJpXeLojqxm77KF4Ak0Ut5QQoQzul3XiXena7PH38D7W19i/b6vARgeP5arMu5sdgaD6H2607ksxLHIudwJGipwLfsvlK8O/5Cp+DNnBjuiHisY1ey7ZDLf2SSZF91OYzWuZX9BeWvwDz4L/+hrOuytWnI+N/jqeC/nebKLVwKQGjOCmRl3EhUS12FxiZ5jc/FqFuc8x67KvEN95gdGD2Na+px2LfjW3a7NWmvWFq3gg60v4Td9RIfEc+3oe+gX1bFtYUXX193OZSGORs7lzmHs24hz5b8A8J5yN2aS9J/vCMFI5oM/J1cIcWK0xrnuRZS3BjOyL/5RM4IaTmFFLm9s+hdVjWUoZXDO4Cs4Y9BlUqxLtNiopEmkJ06goCKXWm+lFHz7gVKKiX3PIiViEK9ufJSKhhKeXv1nLhk+m0n9psq0eyGEEC1iJo/GP/gs7Plf4Fj7Ip6pv4OQqGCHJdpBi74pffPNN+3yZl9//XW7HEeI3sy2fSlGyRYwHPgm3QI2Z1DiCJgBPt/xNs+s+S+qGsuICU3k9kn/j7MGT+v1SZg4cYYyGBybzug+pzA4Nl3OoZ9IiRzIXSf9JyMSxhPQft7b+gJvbXoCr78x2KEJIYToJvyjZmBG9rX6z699EWRydo/Qom9Lt9xyC9dddx3Lly8nEAic0Bv4/X6WLl3KNddcw2233daqIIUQFlVRiGPLuwD4MmeiIzq2GOXRVDSU8syav7A8fzEazdjkKcw7+c/0jxoalHiE6OlCHeHMGvMLLki7FqUMNuz/ln+t+iOldUXBDk0IIUR3YHNag0CGA6M0B9v2pcGOSLSDFk2zf/DBB3n00Ue56667iImJ4aKLLmLSpElkZmbSt2/fJvvv3r2bjRs3snLlSj755BOqq6tJTk7mwQcfbPcPIESv4ffgXPMc6ACBlHEEUqcEJYwN+77lva0v4PE34LKHMi19DqP7nBKUWIToTZRSnJ56Cf2iBvP6xscorSviXyv/wPSRt8jPoBBCiOPSEcn4Mmfi2PAKji3vYsanoWNSgx2WaIMWF8Dzer288sorvPrqqxQWFh5aq2e324mKiiI8PJza2lqqqqoOjd5rrUlNTeX666/n2muvxekMznTg45ECeKI7cKx7Eduu79GhMXjO/h04O6eq9cHzudFfzwdbX2L9PmvZzYDoNGZm/IyY0IROiUOItupJ1+YaTyVvZD/OzoocAE7ufx4XDrsOuyEth3qDnnQui95NzuUg0BrH6qexFWWhwxPwnPVbcEj74PbQbarZr169muXLl7N27Vpyc3NpbPxx3V5ISAgjRoxgwoQJnHXWWUya1H6ViDuKJPOiqzN2r8K59nlA4Z3yb5jxaZ323jExMWwsWM0b2Y9T0VCKQnH24BmcOehybIat0+IQoq162rX5YN2KLwveB6Bf5GCuGX233GDrBXrauSx6LzmXg8Rbh2v5f6EaKgj0PwnfhJuDHVGP0G2S+SPV19dTU1NDREQEYWFhbT1cp5NkXnRlqq4U5/K/ovyN+Idfgj/90k57b1ObrC1ZxnvZC9DaJDoknpmZP2Ng9LBOi0GI9tJTr81bS7N4a9MTNPrrCXW4mZlxJ8PixwQ7LNGBeuq5LHofOZeDxziQh/PrRwCNb8IcAv0nBzukbi8YyXy7lAsOCwsjKSmpWybyQnRpph/HmudR/kbM2MH4h1/UaW9d7ang2TV/ZUnOK2htktnnZOad/BdJ5IXoYkYkjGPeyX8hJXIQDb5aFmQ9zOc73sbUZrBDE0II0UWZ8Wn4R1wCgH3Dq6jakiBHJFpDev8I0YXZt36AUbET7QjFN3EudNK09v01u3hy1QMUVubitIdwVcYdXJ1xF6GOzlmnL4Q4MTGhCdw+6f8xud85aDTL8xfzwrqHqPVWBzs0IYQQXZR/2IWYsUNQ/kYca58H0x/skMQJkmReiC7KKM3Fvu1TAHxjZ6PD4jrlffMObOSp1X+mqrGc+PBk7jv7fxmbPOVQ0UshRNdkNxxcnn4zV2XcicPmJL98M49//3sKK7cFOzQhhBDBoE2M0m0Ye1ZjlG6DI2dsGTa8E+eiHaEYFQXYcz4ITpyi1VrUmk4I0ck8tdYdUjSBgVMw+45vdjdTmxRU5FLrrcTtjCY1ZjiGav09ulV7lvH+1hfR2mRQTDrXj/k5Ce5kWc8mRDcyNvk0UiJSeWXj/3Ggbh/PrvkrF6Rdy6kDLpCbckII0UsYRVk4shZilO+AgBdsTszYIfjGzcJMGffjjmGx+MbdgHPVU9jzPsVMGI6ZmB68wMUJaZcCeN2dFMATXYrWOFb+C9v+bHREHzxn/gbsTds6bi5ezeKc59hVmYcv4MFhczEgOo3p6XMZlXRiXSS01ny2/Q2+LLDuyI5NPo0ZI2/FZtjlfBY9Rm87l73+Rt7JeZbs/d8DMDJxEjNG3iLLZXqA3nYui55LzuWOYRRl4VrxEDRWocMTwO4CvwdVdwBCIvGcef/hCT3gWL8QW8HXaFcknqm/B1dEkKLvvrptATwhRPux7fwC2/5sUDa8E285aiL/xKoH2F6WjdsZSZ+I/ridkWwvy+aJVQ+wuXh1i9/PF/Dyevb8Q4n81MFXcOWoO7AZMnFHiO7MaQ/h6oy7uHTEjdiUnS0lq/nXyj+yr6Yw2KEJIYToKNrEkbXQSuSjB4AzzKq55AxDR/eHxmoc6xc2mXLvy5iJjuiD8lTjWPcSyHhvtyDJvBBdiKrai2PTIgB8GVeio/o12cfUJotznqPWW0VKRCqhjnAMZSPUEU5KRCq13irezXm+RZWs67w1PL/ub2wqXoVN2bly1B1MHTJDpuIK0UMopTi5/3ncNun3RIXEUd5QzJOr/pN1RV8FOzQhhBAdwDiwHaN8hzUif+T3OaXQ4XEYZTswDmw/fJvdiXfirWDYsRVvwpb/RafFLFpPknkhugq/F+eaZ8D0E0jKIDD4rGZ3K6jIZVdlHrGhiU2SbqUUsaGJFFZuo6Ai95hvd6B+P0+u+hO7KvMIsYdx0/j7GJcypb0+jRCiC+kXNYR5J/+FtLjR+E0vizY/xfL8xchKOyGE6GE8VdYaebur+e32EGu7p6rJJh3VF1/GlQA4Nr2NqtzdkZGKdtCqZD49PZ3f/va3x93v97//PSNHjmzNWwjR6zg2vYmq2Y8OicI3/samd1N/UOutxBfw4LKHNLvdZQvBF/BQ66086nsVVuTy5Ko/Ud5QTHRIPLdP+gODY+VnVYieLMzh5sZx/8GZgy4H4PMdb/Nh7gJJ6IUQoidxRYHNCX5P89v9jdZ2V1SzmwODziTQZzToAM41zx79OKJLaFUyr7Vu8S9/+ZIgxPEZRVnYCr4GFL4JNx+z6IjbGY3D5sLjb2x2uyfQiMPmwu2Mbnb7xv3f8dzav9Hgq6Vv5GDunPwAie6+bf8QQoguTynFeUNncunwG1Aovt/9GW9sepyA9BYWQogewYwfavWOrzvQdN271qi6Msy4IZjxQ5s/gFL4xt2ADolC1RbjyH6z44MWrdah0+xrampwOpsW7xKiSzpeL86OUl+OI2sBAP608zETRhxz99SY4QyITqO8oaTJzTKtNeUNJQyMHkZqzPAm21bsfI83sh8noP2MTJzILRN/i/sod2aFED3XyQPOZ2bmzzCUjez937Ng/cN4j3KDUAghRDeiDHzjZkFIpDVN3lsHZgC8ddafQyLxjZ0Fx2pl7HLjmzAHUNgKv8HYu7bTwhcnpsXlqouKig77c319fZPnDgoEAuTn5/PNN98wYMCAtkUoRCdocS/O9mYGcK59HuVrwIxJxZ9+6fFjVQbT0+fyxKoHKKopIDY0EZctBE+gkfKGEiKc0UxLn3NYv/mA6ee9nBdYW7QCgFMHXMiFw65rU096IUT3NrrPKYTaw3ll4/+xvWwTz619kBvG/QfhTmlHJIQQ3ZmZMg7Pmff/+N22vsz6bpuUjm9sy77bmgnD8Q+7APu2j3GsX4g3JhUdFtcJ0YsT0eI+8yNGjDhUbEtr3aJq11prfvvb33LjjTe2LcoOJn3me7fW9OJsL/atH2Lf+gHaHoL3rN+g3Yktfm1zfeYHRg9jWvqcw/rMN/jqeG3jfHaUb0KhuGTEDZzc/7wWv4+cz6KnkHO5eburtvNS1sM0+GqJD0vm5vH3Ex0aH+ywxDHIuSx6CjmXO5g2rar1nipwRVlT609kIMf04/zqfzEqdmLGDsY75d+tNneiWcHoM9/ikflJk35MDlavXk1cXByDBg1qdl+n00liYiJTp07lvPNanjQI0emO7MV58CaVMwzt6I+q3I1j/UI8yWNO7OLXAqpsO/atHwLgH3PdCSXyAKOSJpGeOIGCilxqvZW4ndGkxgw/bLS9suEAC9Y/THHtHhw2J9dk3s2IhA6caSCE6Hb6Rw3ltom/58WshzhQv4+n1/yZm8bdL7U0hBCiu1MGZsKw1r/esOObOBfn8v/CKM/HnrsEf/pl7RefaLMWJ/MLFiw49P8jRozg9NNP58EHH+yQoIToLCfSi7NNF8MjeetwrnkO0AT6n0Sg/+RWHcZQBoNj05vdVlRdwIL1D1PjqSTCFc0NY39JSmRq62MWQvRYie6+3D7pDzy/7r85ULePp9f8hRvH/ZL+UUcpkCSEEKJX0OHx+MZej3PNc9hzP8JMGI4Z347fiUWbtGqo8fPPP+f+++9v71iE6Hxt6MXZalrjWL8Q1VCBDk/AN/ra9jv2D7aWZvHMmr9Q46kkyd2POyb9URJ5IcQxRYXEcfukP9A/aggNvlqeW/sgeQc2BjssIYQQQWb2m0RgwCmAxrH2BauonugSWpXM9+3bt0Vz+IXo8trYi7M1bIXfYCvKAmXDO3EuOJrvF99aK3cvZeH6R/AGPAyJzeDWib+X9a9CCABMbZJfnsPG/d+RX56DeUTXjjCHmznjf01aXCa+gJcF6/+Xjfu/C1K0Qgghugrf6GvQ7iRUQwWOrJebtr0TQdHiafbNWblyJatXr6a0tBSv19vsPkop/vrXv7blbYToMAd7cRolOWhH/8On2h/sxZmUfvRenCdI1ezDsfENAHwjL0fHpLbLccEqOPlx3qt8U/gRABNSzuTy9JuxGW36MRdC9BDNFc0cEJ3G9PS5hxXNdNpDmD3233lr85Nk7/+eN7Ifp85bwykDzg9i9EIIIYLK7sI7cS6uFQ9h27ces+ArAoPOCHZUvV6rvuXX1NRw1113sWbNmiZ9ro8kybzo0n7oxela8RCqcjc6PM6aWu9vRNWVtawXZ0sFvDhWPwumDzNhBIGh7Vcc0hvw8NamJ9lSshqAc4dcxZmDLm9R1wkhRM+3uXg1T6x6gFpvldXO0h6Cx9/I9rJsnlj1AHdOfuCwhN5m2Lk64y7CHZF8v/tTPsxdQJ23mnOGXCnXFSGE6KV09AB8o2bg2PQWjuy3MOOGoCOlWGowtSqZ//vf/87q1asZOHAg1113HampqYSFhbV3bEJ0ivboxdkS9s2LMar3op0ReCfc3LTgXivVeqp4ef3/sqc6H5uyc2XG7Yzuc0qbj2tqk4KKXFRdAO2xNamUL4ToHkxtsjjnOWq9VaREpB5KxkMd4aTYUymqKeDdnOdJT5xw2M+4UopLhs8m3BnB5zve5oud71Lnreay9JubXAsOXi+O1llDCCFEzxAYMhVbSQ5GyWYcq5/De9avrCWpIihalcwvW7aM+Ph4Xn/9daKjo9s5JCE6n5kyDk/ymLb14jwGo3gz9vzlAPjG3wgh7bMGv9pTwbNr/kpZ/X5CHW5mjfk5qTEj2nzcn07HNfFjYG92Oq4QousrqMhlV2UesaGJTUbVlVLEhiZSWLmNgorcJt0xlFKcPXg64c5I3s95gdV7l1Pnq2Vmxp04fvjy1tLp+0IIIXoApfBOuAnXsj9j1BRh3/Iu/syZwY6q12pVplJTU8O4ceMkkRc9yw+9OM1+k6w2dO01quStw5FltXb0Dz4bs09Guxy22lPBc2sepKx+P9Eh8dwx6Q/tlsg/seoBtpdl43ZGkhI5ELcz8tB03M3Fq9sheiFEZ6n1VuILeHDZmy+26bKF4At4qPVWHvUYk/tN5ZrRd2NTdraUrGZB1sM0+uubXC/6RPSX64UQQvR0rgh8428CwL5jOcaBbUEOqPdqVbYycOBAysvL2zsWIXokx8Y3UI1VaHcS/lHT2+WYNZ5Knlv7IAfq9xEVEsctE39LfHhym4975HTcUEc4hmGzpuNGpFLrreLdnOebVMAWQnRdbmc0DpsLj7+x2e2eQCMOmwu3M/qYx8lImsxN4+/DaXORX7GFZ9f8lbc2P3n49ULJ9UIIIXoDM2kUgYFTAI1j3Uvga/53jOhYrUrmb7jhBjZu3Ehubm57xyNEj2IUZWHbswpQeMff1C5rimq91VYiX7ePqJBYbpnwW2JCE9oeLCc2HVcI0T2kxgxnQHQa5Q0lTYrWaq0pbyhhYPQwUmOGH/dYg2NHcsvE3xHujGRH2WbW7/uGCGe0XC+EEKIX8mVciQ6NRdWX4dj8drDD6ZValczPnDmTG2+8kdtuu41FixZRXFzc3nEJ0f15anCsfxUA/7Dz0bGD2nzIWm81z699kNK6IiJdMcyd8FtiwxLbfNwfj9/26bhCiK7FUAbT0+fidkZRVFNAg68O0wzQ4KujqKaACGc009LntLhgXd/IQdw26f8R4gjDF/BSXLeHRn9Dk/3keiGEED2cI+TQdHtbwdcYxZuDHFDv06ICeOnp6c0+r7Xmd7/73TFfq5Riy5YtJx6ZEN2Z1jg2vIry1mBG9sU//JI2H7LOW8Pza/9Gce0eIlzRzJ34W+LCktoh2B/9dDpuqCO8yfaWTscVQnQto5ImcefkBw4VqqsIlOKwuUiLG8209DknXKguPqwP142+l62lWfgCHnZX5tE3ajBhDvehfeR6IYQQPZ+ZMAz/4LOx5y/HkfUynqm/B2fT75CiY7QomU9ObvtaXCG6uvZsrWTbsxpbURYom3XH0uZoU2z1vlpeWPffFNfuxu2M4pYJvyU+rE+bjtmcg9Nxt5dlk2JPPWzq7MHpuGlxo1s0HVcI0bWMSppEeuKEdrvOjUqaxJjk01hf9BUBHWBP1Q5SIlJxu6LkeiGEEL2If+R0bCVbULXFOLLfxDfh5mCH1Gu0KJlftmxZR8chRFC1a2ulhgrsG18DwD/iYnR0/zbF1uCr44W1/82+mkLczijmTvhNuxS7a87B6bhPrHqAopoCYkMTMWwRNPjqKG8oOeHpuEKIrsVQRpP2c2051lWjbqekdg/7agoxdYA91fnEhfXBG2iU64UQQvQWdife8Tfh+vLv2HavJJA8BjNlXLCj6hXkN6zo9dq1tZLWOLMWonwNmNED8add0KbYGnx1vLDuIYpqCgh3RjJnwq9JdPdt0zGP5+B03KFxmdR6q9lXXUitt5q0uNHcMfmP0jdaCHHIqKRJ3HXSfzIu5XQcNheN/gb21RSS5O4v1wshhOhFdOwg/MPOB7BqRnlqghxR79CikXkheqojW7EdnFYe6ggnxZ5KUU0B7+Y8T3rihBaNLtkKv8Eo2QyGHd+Em8CwtTq2Rn89L2X9nb3V+YQ5Ipgz/lckufu1+ngn4qfTcZUrgPbY2jQdVwjRcx28XuSX5/BB7ovsqsgjxB5KmNN9/BcLIYToMfzDL8HYvwmjei+ODa/im3QbHNHtRLSvViXzixcvbtF+DoeD6OhoRowYQVxcXGveSogOdSKt2I43NVXVHcCR/RYAvpHT0BGtnwrv8Tfw4rq/s7tqB6EON3Mm/Io+EQNafbzWODgdNyYmhoqKik59byFE92Iog6Fxo7jn5L/yyob/I/fAehZk/S9zJ/yGflGDgx2eEEKIzmBz4Bt/E64V/42tKIvAntWY/ScHO6oerVXJ/K9//esmic/xnHLKKfzud79jyJAhrXlLITpES1qxVQRKj99aSWsc616CgAczbiiBIVNbHZPX38iLWX9nd9V2Qu3hzBn/a5IjBrb6eEII0Vlshp1rR9/DgqyHya/YwotZf+fWib/rtFlFQgghgktH98c/4mLsOe/j2PganvhhEBod7LB6rFbNmZ03bx7Tpk1Da01YWBjnnXceN910EzfeeCPnnXce4eHhaK25/PLLueqqqxg6dCjffvsts2fPpqioqL0/gxCt9tNWbM1paWslW/5yjLI8sLnwjbsBWjkd3etv5KX1D7OrMo8Qexg3T/gVKZGSyAshug+Hzcmssb+gX+RgGn7oxFFeXxLssIQQQnQSf9oFmNEDUb4GnFkvg9bBDqnHalXGcfnll7Ns2TKuvvpqVqxYwaOPPsqvf/1rfvOb3/Doo4/yxRdfcM0117B8+XJuueUW3nvvPW655RYqKip46qmn2vszCNFqB1uxlTeUoI+40BxsrTQwetgxWyupmmIcmxcD4Mu4Au1ObFUs3oCHBev/l4KKrbjsodw8/lf0jRzUqmMJIUQwueyh3Dj+PpLc/ajxVPL8ur9R1Vge7LCEEEK0B21ilG7D2LMao3QbaPPw7Ybth9pRdoySzdgKvw1OnL1Aq5L5hx9+mJiYGP70pz/hdjctcON2u3nggQeIiYnh4YcfRinFv/3bv5GYmMjXX3/d5qCFaC8HW7G5nVEU1RTQ4KvDNAM0+Oooqik4fmslM4Bj3Ytg+jATRhBIPb1VcfgCXl5e/wg7K3Jw2kK4efz9ss5UCNGthTnc3DT+fmJDk6hoKOWFdf9NnVeqGwshRHdmFGXhWnI/ro9/hWvpA9bjkvsxirIO209HJOMbOQ0AR/abqPqyYITb47UqmV+5ciUZGRnHXDevlCIjI4NVq1YBYLfbGTFiBCUlMtVOdC1HtmLbX7u7xa3YbNs/w6jYiXaE4h1/49Erdh7jDubBRD6/fDNOm4ubxt9H/6ih7f0xhRCi00W6Ypgz4VdEumIorSvixay/0+CrC3ZYQgghWsEoysK14iGMki1oVyQ6si/aFYlRkmM9f0RCHxgyFTNuKAQ8ONa+KNPtO0CrCuB5vV72799/3P3279+P1+s99OfQ0FAcDkdr3lKIDvXTVmy13krczujjtmJTVXtx5HwAgD/zagiNaXY/oygLR9ZCjPIdEPCCzYkZOwTfuFl4kkbxyob/Y0f5JiuRH3cfA6OHdchnFEKIYIgJTWDOhF/z9Oq/UFS9k5fXP8JN4+/DaXMFOzQhhBAtpU0cWQuhsQodPeDHASxnGNrRH1W5G8f6hXiSx/xYO0oZ+MbdgGv5XzHK8rDlL29TkWjRVKtG5keOHMm6dev49NNPj7rPp59+ytq1axk1atSh54qKioiPj2/NWwrR4Q62Yhvd5xQGx6Yfu6e66cex9gXQAQJ9RhPof1LzxzzGHUzbF3/jtW9+S17ZRhw2JzeM+yUDj7E2XwghuquE8BRuHn8/LnsohZW5vLrhUQKmv0WvNbVJfnkOG/d/R355DuaRazOFEEJ0OOPAdozyHejwhKYzUZVCh8dhlO3AOLD9sE3anYgv4woAHJsXo2qKj/1Gx1uPLw7TqpH5u+++m1tuuYWf//znnH766VxwwQUkJyejlKKoqIhPPvmEr776CpvNxrx58wAoLy9ny5YtTJs2rV0/gBDBYN+6BKN6D9oRjm/srOan1x/jDqbP3peXK9eQt3s79j5juGHsfzAo5th97IUQojtLiUzlxrG/5Pl1D5FXtpE3N/2LqzPnHfPG6ebi1SzOeY5dlXn4Ah4cNhcDotOYnj73mEughBBCtDNPlTXD1H6UWVX2EKgvs/Y7QiD1dGxFWRilW3GsexHv6b8Ew9Zkv2PNZjVTxrX3J+oRWpXMn3LKKTzyyCP84Q9/4Msvv+Srr746bLvWmqioKB544AFOOeUUAHw+H3//+98PG6kXojtSFTux530CgG/s9RAS2ex+R7uD6dcmL3vy2GLz4vCazB54FYNiJZEXQvR8A2OGM2vMz3l5/SNsKl6FyxbK9JG3NFuDZ3Pxap5Y9QC13ipiQxNx2UPw+BvZXpbNE6se4M7JD0hCL4QQncUVBTYn+D3gDGu63d9obXdFNd2mFN7xN+Ja9meMip3Ytn9GYNiFh+1ycDYrjVXWd2e7C/yeQ+vxPWfeLwl9M1qVzANccMEFnHbaaXz00UdkZWUdKmyXkJDA+PHjufDCC4mIiDi0f1JSEhdffHHbIxYimAJenGtfBG0S6DsRs+/4o+/bzB1MvzZ5tTGPLf4K7Iadm80YBoYkIBOIhBC9RVr8aK7OvIvXNv6TtUUrcNlDuWjY9Ycl9KY2WZzzHLXeKlIiUg9tC3WEk2JPpaimgHdznic9ccKxl0QJIYRoF2b8UMzYIRglOWhH/8NnpWqNqivDTErHjD9KEefQGPyZV+NY9yKOnA8wkzLRUX1/eH0r1uMLoA3JPFgt6GbOnMnMmTPbKx4hujR7zvuo2mK0KxLfmGuPvfMRdzADWvNa43ay/eXYUNxoH8gwFJ7m7mAKIUQPNippEjNG3caizU/x7a6PCbGHMXXIjEPbCypy2VWZR2xoYpNRe6UUsaGJFFZuo6Ail8Eys0kIITqeMvCNm4VrxUOoyt3o8Dhrar2/EVVXBiGRPyw9PXqyHeh/EkZRFrb9G3GsfQHvWb+yetGfwHp8M0EKRf+U3NoQooWMA3nYt38OgG/cbHCGH3P/g3cwVd0BTNPk9cY8NvrLrEQ+ZBjpjT7MuCFHv4MphBA92PiU07lk+A0ALMtfxDeFHx/aVuutxBfw4LKHNPtaly0EX8BDrbeyM0IVQggBmCnjrOnuiekoTw2qei/KU4OZlN6yafBK4Rs7C+0Ix6jeg33rEuv5lqzHD3ibXY/f27VoZL6oqAiwpsrbbLZDf26plJSUE49MiK7E78Gx7iVAExhwKmafzOO/5id3MD+ozGK9rR6bYecG+wBG1ta26A6mEEL0ZKcMOJ9Gfz2f73ibj7YtJMQeyoS+Z+J2RuOwufD4Gwl1NL1x6gk04rC5cDujOz9oIYToxcyUcXiSx1hV6z1V4IqyBqZa+n02JBLf2Otxrn4ae94nBJIz27Yev5drUTI/depUDMPgww8/ZNCgQUydOrXZYjXNUUqxZcuWNgUpRLDZNy9C1R9Ah8biy7yqxa8zU8axPP0Mvt60DeULcJ0ZxygMzKR0fGOlMqcQQpw1aBqN/nq+KfyIxVuexWkLYVTSJAZEp7G9LJsUe+ph3zm01pQ3lJAWN5pUaecphBCdTxltmu5u9h1PoGgitr1rcK59Ec9Zv27bevxerEXJ/KRJVrXY0NDQw/4sRG9glGzBvvNLAHzjbwBHaItfm1Oyjg9Lv0MnpXNh4hmMiBuH50TvYAohRA+mlOLCtOvw+BtYs/cL3tr0BC57CNPT5/LEqgcoqimwqtnbQvAEGilvKCHCGc209DlS/E4IIbop35hrMQ5sQ9UWY8/5oM3r8XsrpbXWwQ4i2CoqKtr1eDExMe1+TBEkvnpcy/6CaqjAP/gs/KOvafFL91bv5Jk1f8EX8DKp79lcnj6nxTNauhI5n0VPIedy12ZqkzezHye7eCV2w8nN4++jzlvTpM/8wOhhTEuf06vb0sm5LHoKOZd7N2P/JpzfPwYovFN+Ad66pn3m44Z0m9ms7X0+x8TEHHefNlWzF6Knc2S/iWqoQIcn4B85vcWvq2w4wIKsh/EFvAyNy+DSETd2y0ReCCE6i6EMrsq4E2/AQ+6B9byU9TC3TPwtvznzMQoqcqn1VuJ2RpMaM1xG5IUQogcw+2QQGHgatsJvcKx7Cc/U/4fn4odavx6/F2qXvxmv10tJSQmVlZXtcTghugRj3wZsu74HFN7xNx29wuYRGv31LFj/v9R6q0hy9+OazLuxGXLfTAghjsdm2Ll29D2kxozAG2jkhXUPcaBuH4Nj0xnd5xQGx6ZLIi+EED2IL+NKdGgsqr4M+6a3D63HN/tNstblyzX/mNr0t/PKK68wbdo0xo4dy5lnnsl///d/H9q2ZMkS7r77bgoKCtoaoxCdz1OLI2shAP6089BxQ1r0soDp5/WN8ymu3Y3bGcUNY3/ZbCVmIYQQzXPYnMwe+2/0jRxMg6+W59f9jYqG0mCHJYQQoiM4QvGNvxEAe8FXGMWbgxxQ99KqZN7v93PnnXfy5z//mZ07dzJkyBCOXHo/ZMgQli5dypIlS9olUCE6jdY4NryK8tZgRiTjH3FJC1+m+WDrS+SVZeOwOblh3C+JDo3v4GCFEKLnCbGHcdP4+0gM70uNp5Ln1v6Nao+sqxVCiJ7ITBiOf/DZADiyXgZffZAj6j5alcy/9NJLfPHFF5x11lksX76c999/v8k+w4cPp1+/fnz55ZdtDlKIzmTsXYOtaB0oG77xN1l9LVvgm8KPWL13OQrF1Rnz6Bs5qIMjFUKInivM4ebmCb8iJjSRioYSXlz3dzz+hmCHJYQQogP4R05HhyeiGitxbHwj2OF0G61K5hcvXkxCQgKPPPIIcXFxR92vf//+FBUVtTo4ITqTaWoKtlZQt+I16urAm3YhOmZgi167uXg1H+e9CsBFw2eRnji+I0MVQoheIdIVw9wJv8btjKK4djdvbnqiyUxAIYQQPYDdiXfCTYDCtnslRlFWsCPqFlqVzBcWFjJmzBhCQkKOuZ+0mxDdRfYmzX/+l0n2C6+yc1sdX2/pxwNvn0/2puN/adxdtZ03Nz0BwMn9z+OU/ud3dLhCCNFrxIQmMGvsL7AbDraWrmPpjreCHZIQQogOoGMH40+zvkc7NrwG3rogR9T1tSqZdzqd1Ncffy1DUVERERERrXkLITpN9ibN/Mc1tqJ1pEdsxOmys7zxJnLz7Mx/XB8zoa9oKOXl9Y/gN70Mjx/LxcNnSws6IYRoZ/2jhjJ95FwAVux8j437vwtyREIIITqCf8QlaHcSylONY/M7wQ6ny2tVMj9s2DCys7OPOepeVFTEli1byMjIaHVwQnQ009S8/Y7GV1/HFf3fxGZAlvd86p0ppKRAbS0sekdjmk0T+gZfHS9l/Q913mqSIwZyTea8Tm+ZZJqaHfma9Rusx+biFEKInmBs8hROT7UKki7a/Ax7qvKDHJEQQoh2Z3PgHTfb+t/CbzBKc4McUNfWqszjqquuoqamhvvuu4+qqqom2+vq6vj973+Pz+dj5syZbQ5SiI6yswAKC2Fav3cIU9VUmH1Y67kAAKUgJhYKCq39fipg+nl1w6OU1hUR6Yph9th/x2k/9rKT9mYtDdD85a+av/+v9fif/3XsmQRCCNGdnTf0aobHj8Vvelm44RGpcC+EED2QjhuKP/V0ABzrF0LAG+SIuq5WJfMzZszgggsu4Ouvv+acc87hjjvuACArK4t7772XqVOn8u2333LJJZdw3nnntWvAQrSnmhpItuWSGf4tAF80XI+J49B2lxO8Pmu/g7TWvJvzPPkVW3DaXNww7pdEhcR2atwHlwZsywO3G5L7WI95eRx3aYAQQnRXhjKYmfkzEsJTqPFUsnD9P/DJlzwhhOhx/KOmo0OiUHWl2LdKq/OjafWc4H/84x/88pe/xOFwsGLFCgAKCgr49NNPMU2Tn//85/z9739vt0CF6AiR4V4uT3wF04RN3jPYHxh62HaPF5wO+GnphxU732Nd0ZcoFNeOvofkiONXvDe1SX55Dhv3f0d+eQ6mNlsd88GlATW10DcFQkPBMKzH4y0NEEKI7i7EHsYNY39JqMPN3up83tnyTKsq3LfndVkIIUQ7c4ThG3MdAPbtn6Gq9gQ5oK7J3toXKqW47bbbmDt3Llu2bGHv3r2YpklSUhKZmZk4nS3rzS1EMKU1LsEeUcq+6mi+918OP6ldpzVUlMOwNBiUaj23cf93hyopXzriRobFjznue2wuXs3inOfYVZmHL+DBYXMxIDqN6elzGZU06YRjPrg0IC7WWgrwU0cuDRgy+IQPL4QQXV5sWCLXjb6HF9Y9xMb935Hk7seZgy5v8evb+7oshBCi/ZnJYwikjMdWtA5H1st4z7gPDFuww+pS2lyty2azkZmZyYUXXsjFF1/MhAkTJJEX3YKq3I1jx1L69FGsqLuWgqIw6hsgEID6Bigqskbkr5ihMAxFYUUub296CoDTBl7ESf3PPe57bC5ezROrHmB7WTZuZyR9IvrjdkayvSybJ1Y9wObi1Sccd02NNfXf5Wp+e3NLA4QQoqcZHDuSS4ffAMDS7W+RU7KuRa/riOuyEEKIjuEbfTXaEYpRWYgtf3mww+lyOrf0thBdhRnAsf5l0CbhI8Zz0S1jSEuDulrYX2w9DkuDeT9TZGYoyuqLeXnDPwhoPyMTJ3Jh2nXHfwttsjjnOWq9VaREpBLqCMdQNkId4aREpFLrreLdnOdPeGpnRIQ19d/jaX57c0sDuhKpwC+EaC+T+5/D5H7noNG8uelx9tfsOub+HXVdFkII0UFCovCPuhIAx5b3UHWlQQ6oa2nRNPv58+e3+g2UUsybN6/VrxeiI9jyl2NU7kI7QvGNvprMEMWokdbU9JoaKxEelAqGoaj31fJS1v/Q4Kulb+Rgrsq4s0W95AsqctlVmUdsaGKT/ZVSxIYmUli5jYKKXAbHprc49kGpMHCgVewuJeXwqfbNLQ3oSrI3Wev9Cwut2QNOh/VZrpwBmRnH/zsVQogjXTJ8Ngfq9pFfsYWX1z/CnSf9Cbczstl9O+q6LIQQouMEBp6Kbc8qjAPbcKx/Be+p9zZda9pLtTiZV0o1W2Dm4C/DI7cd3F+SedHVqLpSHFveA8CfcSWERAFW4n7kGnO/6WPh+n9QVr+f6JB4Zo/9N5y2o8xvP0KttxJfwIPrKC3rXLYQKgKl1HorTyh+w1BcOcOqWl9UZK2RdzmtEfmK8sOXBnQlByvw19Ra6/1dLmt2wcEK/HffJQm9EOLE2Qw71465hydWPkB5QzGvbfwnc8b/CpvR9CtOR12XhRBCdCCl8I2dhWvZnzFKt2LbvZLAgJODHVWX0KJk/u677272ea01jz32GOnp6ZxzzjntGpgQHUJrHOtfAdOHGT+MwIBTj7GrZtHmpymszMVlD+WGcb8kwhXd4rdyO6Nx2Fx4/I2EOsKbbPcEGnHYXLidLT/mQZkZirvv4tAod8UPo9zD0qxEvqslxUdW4D94M/VgBf6iIqsC/6iRdLmbEEKIri/M4Wb22H/jiVUPUFCxlfe3vsi09LlNRt878roshBCi42h3Ir4Rl+LYshh79lsEkkaBq4uuKe1EbUrmAR577DFGjBhxzH2E6Cpsu1dilG4Fw45v7KxjTtFZlr+Ijfu/w1A2rht9L0nufif0XqkxwxkQncb2smxS7KmHfanUWlPeUEJa3GhSY4a36rNkZhx9aUBXIxX4hRAdLdHdl2tGz+PlrP9lzd4vSHL355QB5x+2T0dfl4UQQnScwNBzsO1Zg1G9B0f2m/gmzg12SEEnBfBE7+GpwZ5ttZXzjbgU7U486q5ZRV+zPH8xANPS5zA0LuOE385QBtPT5+J2RlFUU0CDrw7TDNDgq6OopoAIZzTT0udgqNb/GFpLAxRjx1iPXTGRB6nAL4ToHMPjx3JB2rUALNm2kO1lmw7b3hnXZSGEEB3EsOMbNwtQ2Pasxti/6bgv6em65G+r4uJiXnjhBebOnctZZ51FRkYGp512Gvfccw8bNmxo9jW1tbU8+OCDnH322WRkZHD22Wfz4IMPUltb28nRi67Kkf0myleHGdmPwNCjLwvJL89h8ZZnATgj9TIm9D2z1e85KmkSd05+gKFxmdR6q9lfu5tabzVpcaO5Y/Ifu3w/Y79fN1sr40R19wr8Qoju47SBFzE2eQpam7y28Z8cqNt32Pbufl0WQojeTMek4h8yFQDHhlfA1xjkiIKrRdPsO9uCBQt4+umnGTBgAKeeeipxcXEUFhaydOlSli5dysMPP8zFF198aP/6+npmz55NTk4Op512Gpdccglbt27lhRdeYOXKlbzyyiuEhYUF8ROJYDOKN2PbsxpQ1h29ZgojAZTWFfHKDy3oMpNO4ryhM9v83qOSJpGeOIGCilxqvZW4ndGkxgzvMiM/WmtqaqC4BEpLoaQEiks1paVQXQ2hITU4nCbucAgPh/BwRYQbwsLBHQ5ut/W82w3hYRASQpN1qt25Aj9Ya/67w3IGIYR1/ZmWPoey+v3srtrOy+sf4Y7JfzxsjXxXvy4LIYQ4On/6ZdiKslAN5dhz3sM/+upghxQ0XTKZHz16NAsXLmTixImHPb9mzRpuvvlm/vSnP3HuuefidDoBeOaZZ8jJyeHWW2/lvvvuO7T/o48+ymOPPcYzzzzDvffe26mfQXQhfo9V9A7wD5mKjkltdjePv4GF6/9Bo7+eAdFpXDHq9ha1oGsJQxlBb3MUCGjKy6Hkh4S9tPTHpL3xGDc1/QFoqLYSe8uxR+ptNnCHa8Ld/HADQOEOh9QfkvmdOyE6xtrmD3TtCvwg7fSE6I4cNifXj/k5T6z6Iwfq9/FG9mPMHvtLbIbt0D5d4boshBCiFewufONm4fz2n9jzvyDQbxI6dlCwowoKpds4h3bEiBHMmDGDBx98sL1iOqZbbrmFr7/+mrfeeovMzEy01pxxxhnU1tbyzTffHDYC7/F4OP300wkJCWHFihVHTcwqKiraNcaYmJh2P6ZoPfvGN7DnL0eHxuI55w9gb7pwW2vNG9mPkV28kqiQWH520p+P2qe4q/N4NAcO/GSkvRSKSzRlZRAINP8apSAuDhITFEmJkJAAiQkQHw9hYdHs3VtJTS3U12M91lmPdXVQV6eprYXaOvB6jx1bTQ3s2w8N9WBqaxQ/dQDMngUnTe56I2JHa6dXXm7NRLj7rq7XOUAcnVybe5+i6kKeWv2f+E0vpw64kIuHzwp2SO1CzmXRU8i5LNrCsfYFbLtXYkak4D37N0ededtZ2vt8jomJOe4+XXJk/ljsdvthjwUFBZSUlDBlypQmU+ldLhcTJ07k888/p7CwkNTU1M4OVwSZKt+JPf8LAGt6fTOJPMDKPUvJLl6JoWxck3l3t0nkAwHN1lzIz4eSUk1xCVRWHn1/h+Ngov6TpD0R4uPAbm8+KY2JMYBjJaw/bvP5NLV1UPtDol9bC3X1UFf74//X1Gj27YeKCrDbrfZ0ixZD1nqTjAxFxkiIjg5+gizt9ITo/lIiB3LlqNt5PXs+3+76mCR3vzbVQRFCCNF1+DKuwijejFFThD3vU/zDLz7+i3qYFiXz6elHn4amlGLx4sUsXrz4qNu3bNnSquCOVFRUxLfffktCQgLDhg0DoLCwEOCoifrAgQMP7Xe0faKiojCM9h0VbMmdFNE62jShJBfdWIkKiYbE4ahm/v10wI/5zVtohx016DRCh5/W7PEKy7exdOebOBwOpmfOYcygyR38CdqurMzku+99fPOdj6oq87BtDge43Yo+SQZ9+tjok2SQlGTQJ8kgNla1aunAiZzPR+8RcLjSUpOsDT6ysvwU7gqwew/s3gMffQyDUm2MG2tn3FgHcXHBGbHfludnz55akhIVTmfTv7PERM3uPZqycjfD0rrdfdFeS67Nvc8ZMRdSRyWfbH2dj3YsZEjycAbFdf/p9XIui55CzmXRejGYp87F/OZfOPKXYks/GxWVEtyIOvl8btE30LbMxG+PStgAPp+P+++/H6/Xy3/8x39gs1nr3mp+6GXldrubfd3B52uO0fOqqqqqXWI8SKYMdRyjKAtH1kKM8h0Q8ILNiRk7BN+4WZgp4w7b1577EfbSfLTTjWfIJdZQ8BHqfbU89f1fafQ0MDJxEqNjp3TZf7uDo/ArV0HuNs3BH63wcBiTqUhOtkbZExOsderNOdao/dF01Plst8OkCdZ/FRWaTZutae0FhbAtz8e2PHj9TejXFzIyFKMzID6+80bA9+7VNDRqoqPB52u63TCgoRH27q0moRPjEq0n1+be66Sk88kvzmVLyWr+9dWfuXPyA8SEJgQ7rFaTc1n0FHIuizaLGoEzZhhGyWY8XzyG9/RfHl5puRN12Wn2W7dubXMwbWGaJr/97W9ZvXo1V199NdOnTw9qPCI4jKIsXCsegsYqdHiCNWXe78EoycG14iE8Z95/KKFXNcXYt34IgC9zJria3uzRWvP2piepbDxAbGgSM0be0m4F79pTZaVm1WpYvUZTVf3j80OGwMmTFaNGHn2KfHcRE6M4fQqcPkVRXf1jYp+/E/bshT17NR9/Asl9NJkZisxMSErs2M/803Z6oaFNt0s7PSG6D6UUV426nacbSthXU8jCDf/g9on/D6c9JNihCSGEaAul8I29Dtfnf8Yo34Gt4CsCg84IdlSdpsvPDdVa8/vf/5733nuPyy+/nD/96U+HbY/44Zv00frJH3w+Qr5xd2/axJG10Erkowf8eMfNGYZ29EdV7saxfiGe5DGAwrF+IegAZuIozH7N9wz+suB9cg+sx244uXb0PYe1LQo20/xxFH5r7o+j8GFhMHGC4qRJkJDQvRP4o4mMVJx6Cpx6iqK21krsN23WbN9hFc/bt1/z6VJITNBkZCgyMyAluWk7vLbq7u30hBCHc9pDmDXmF/xr1R/ZX7OLtzY/yXWj7+2SN3GFEEK0nA6LwzdyGo7sN7BvfodAn0wI7R3LN7p0Mm+aJr/73e9YtGgRl156KX/729+arG0/uCa+oKCg2WMcXFN/cD/RPRkHtmOU77BG5I/84qUUOjwOo2wHxoHtqNpijLI8sLnwjb2u2ak2+eU5LN3+FgCXjbiRlMiucX5UVmpWr4XVqzWVP1n9MXgQnHySImNU9x+FPxFut+Lkk6zPXl+v2ZID2ZtgW56mpBSWLdcsW25V4s8cBZmZ1rT89vhybhiKK2fA/Mc1RUUQEwsupzUi39Xb6QkhmhcdGs/1Y37Oc2seZEvJGpblL+KcIVcGOywhhBBtFBh8JrY9qzEqduLY8Bq+k+4M2nT7ztRlk/mfJvIXX3wxDz300KF18j+VmppKYmIi69ato76+vklrujVr1pCYmCjJfHfnqbLWyB+lGj32EKgvg+q92HPeA8A38nJ0WFyTXas9FbyR/RgazbiU0xmfEtypOKapyd1mjcLnbD1iFH684qTJPXcU/kSEhSkmToCJE6ChAXK2wqbNVv2AsjL44kvNF19CTAycOxUmjG97lfnMDMXdd3Goz3zFD33mh6VZiby0pROi+xkYPYxpI+eyaPNTLM9fTGJ4XzL7nBzssIQQQrSFMvCNnYXriwex7d9IYN/6JvW0eqIumcz/NJG/8MIL+fvf/95sIg/WCNzMmTN57LHHeOyxx7jvvvsObXvyySepqqpi3rx5Mo2uu3NFgc0Jfg84w5pu9zeCzYm98FuUvxEzJpXA4LOa7BYwA7yR/Ti13iqS3P25bMRNQTs3qqo0q9fAqjX6sMJ0gwfB5EnW9HGHQ87b5oSGKsaPg/HjrDXtW3OtEfutuZqKCnjzbc0338Hll8DgwW1P6EeNhJ0FUFNjjcgPSpV2dEJ0Z+NTTqe4djffFH7E25ufJiE8hT4RA4IdlhBCiDbQUX3xp52PfdtHODa8hid+GDi7zjLajqB0e5Wbb0f//Oc/mT9/PmFhYdx4442Hesr/1LnnnnuoZV59fT3XX389OTk5nHbaaYwaNYqtW7fy5Zdfkp6eziuvvNKkB/1PtXcVTanM2QG0iWvJ/RglOejo/k0WMKvK3eiIJCvhN+x4zvoNOqpvk8N8mvcGXxa8j9Pm4q6T/kx8eHInfghrFH5bHny/0ko8zR+6yoWGWqPwkyd3fGG3E9WdzmefT/Pd9/D5Mk1Do/VcZobiogshPq5r/b2KztedzmXR8UxtsiDrYfLKNhIfnsxdk/+z2xTEk3NZ9BRyLot2F/DiWv5XVG0xgYGn4Rs3u9PeustWs+9se/fuBawk/Yknnmh2n759+x5K5sPCwliwYAHz58/nk08+YdWqVcTHx3PzzTdz9913HzORF92EMvCNm4VrxUNW4h4eZ02t9zei6sqsu26GHZTCn3Zes4n81tIsvix4H4AZI2/r1ETe59N89Q2sXKUP65CXmmpVpJdR+LYzTc2u3RAZCVddaRWuW7naqoq/JQemnAZTz7JG9YUQwlAGV2Xcyfzvf8uBun18kLuAK0bdFuywhBBCtIXNiW/sLJxf/y+2wm8I9JuEmTA82FF1mC45Mt/ZZGS++2i2z3zcEHR4AkZFAdqdhOfs31oj9D9R0VDK49//Pxr8dZzc/3wuHXFDp8W8fYfm7Xesdd0AoSEwYYJi8iTok9T1E8vucD5nb9KH1rV7f1jXPnAgnHUmbN8O2/Ks/dzhcP55ikkTwWbr+n/3on11h3NZdL6dFTk8t+ZBNJqrMu5kbPJpwQ7puORcFj2FnMuiozjWL8RW8DU6PAHP1N83yQ06gozMC3EcZso4GvpksmvnZ9TU7ScivA+pEamEfPsoAL6x1zf5YfWbPl7bOJ8Gfx39Igdz0bDrOiXWhgbNhx/BqtXW/bLISLjwfMWY0TIK356yN2nmP66pqYW4WHC5rHX0eXmwbx/M+xlMOVXx/oea0gOwaLE1Ff/SiyEtTf4dhOjtBsWkc9bg6SzPf4f3cp6nX9QQ4sP6BDssIYQQbeAbNQNjfzaqrhR77hL8I6cHO6QOIcm86FY2F69mcc5z7KrMwxfwEGI4mVejGeZKImz4pZjxw5q85qNtr7C3Op9Qh5trRt+Nzej4037TZs3i9zTV1dafTz5JcdEFMsW7vZmmNSJfUwt9f9ILPjTU6g1fVATvLIb/9zv4t6GK71fBZ0s1+/bD089pRqbDJRdJtwAheruzBk1jZ0UOBRVbeWPjY9w++Q/YDUewwxJCCNFajjB8o6/FuepJ7HmfEeg7ER3VL9hRtTvj+LsI0TVsLl7NE6seYHtZNm5nJH0i+nNWI9jqDrClejsbE5q2H9y4/ztW7l4KwMyMO4kJTejQGKtrNAsWal562UrkE+LhztsUV0xXksh3gJ0FUFhojcgf2ZRAKas3fEGhtZ/drphyquJX/6GYcprCZsCWHM3//kPz/gea+vpev+JIiF7LZtiYmXEnoQ43RTUFfJr3RrBDEkII0UZmylgCKeNAmziyXgYzEOyQ2p0k86JbMLXJ4pznqPVWkRKRSqgjnHifn0l19YTYw1gSZvBO3iuY2jz0mtK6IhZveRaAMwddzrD4MR0Wn9aa1Ws0D/+vJnuTxjDg7LMUv7hXtbk1mji6mhprjbzL1fx2l9PaXlPz43NhYYrLL1X8+y8U6SMUARO++kbz0MOab77V+P2S1AvRG0WFxHHlqNsB+HbXx2wtXRfkiIQQQrSVL/NqtCMUo7IQW/7yYIfT7iSZF91CQUUuuyrziA1NRCmF0prTy0pQWlMQ5uZATF8KK7dRUJELgNffyKsbHsUb8DA4ZiRTB1/RYbEdKNM89azmzbetdmj9+sK9dysuukB167XxpqnZka9Zs9bHjnyNaXa9JDciwip25/E0v93jtbZHRDTdlpCgmHOT4ra5ij5JUF8P776v+cejmq25Xe+zCiE63oiEcZw64EIA3t78NFWNZUGOSAghRJuERuMfZeUBji3voepKgxxQ+5I186JbqPVW4gt4cP3QA3hkTRUJnkZ8hsG3sQm4DEVFoJRabyVaa97b+gIldXtxO6OYmfkzbIat3WMKBDRffwuffqbx+cDhsCqlTzm1+1dK/2l1+ECgFptNM3AgXDnD6tveVQxKtarW5+VZa+R/OtVea6goh2Fp1n5Hk5am+Pk9sGqN9W9ZUgrPvaAZPkxz6SWKpMSu83mFEB3v/LSrKajYSlFNAW9k/4u5E37TIb9DhBBCdI7AwNOw7V6JrWgDzi//B9/EWzDjh4Lq/uPa3f8TiF7B7YzGYXPh8TcS7vcxqcIaLVkZE0+93Y4n0IjD5sLtjGbN3i9Yv+8bFIprRs8jwhXd7vHsLdI89i/Nh0usRH7IEPi3nyvOPF31iER+/uOabXngdkPfvgZut5Uwz3/cWkbQVRiG4soZCrfbKnZX3wCBgPVYVGSNyF8xQ2EYx/43sdkUp5ykuP+XijNOV9hskLsNHvk/zeJ3NXV1XeczCyE6lt1wcM3ou3HaXBRW5vLFzneDHZIQQog2MPatR5XtxCjNwb59Ka6P7sO15H6Moqxgh9ZmksyLbiE1ZjgDotMobyjh5PID2LVJsSuEre5ItNaUN5QwMHoYLnsIH+YuAOC8oTMZFJPernH4fJqPPtH88zHNnr1Wz/iZVypuv0URH9e9k3hoWh0+NNRKmA9Wh6+thUXvdK0p95kZirvvUqSlQV0t7C+2HoelwbyfqROaSRAaqrj0YsUv/02RMUphmvDt95qH/kfz1deynl6I3iIuLIlp6XMB+CJ/MfnlOUGOSAghRGsYRVm4VjyEUZFPIGYQgfhh6JBojJIc6/luntDLNHvRLRjKYHr6XJZ8+RuSq0vw21x8FRNHg7+e8oYSIpzRXJh2Ha9tnI/f9DE8fiynp17arjHk51uJbukB68+ZGYppl0FkZPdP4g86kerwQwYHI8LmZWYoRo204qqpsUbkB6Vy3BH5o4mPU9w4G7bvgA+WaIqK4P0PNWuz4KYbICa65/ybCyGaNyb5VHaUb2Zd0Ze8uelx5p38X7idkcEOSwghREtpE0fWQmisQkcPOOzLrXb0R1XuxrF+IZ7kMd12yr0k86LbGBU/llTXMModjXztDLDFW4LD5iItbjSXj7iZDfu/paKhhOiQeK7MuAN1ZDbaSg0Nmo8+ge9XWqOykZEw/XJr5LanaUl1+IojqsN3FYah2v0Gw9Ahinvnwdp1sORjK6n/53zN7OuRLgVC9AKXDr+BXVV5HKjbx6LNT3HD2F+22+8WIYQQHcs4sB2jfAc6PKHZUSodHodRtgPjwHbMhGHBCbKNJJkX3YZ9+1LiTE1M39PwT7iOUWYDbmc0qTHD+W7XJ+SUrsWm7Fw7+h7CHO52ec/NW6w101XV1p8nT1JcchE9tmf8T6vDh4Y23X6s6vA9lWEoJk2EoUPgxZethP7pZzWXXw6nnNQzzwMhhMVpD+HazLt5YtUDbDuwgW93fcxpAy8KdlhCCCFawlMFAS/YjzJKZQ+B+jJrv26qe84nEL2Oqi/DnvsRAP7MqxiUOJbRfU5hcGw6u6u283HeawBcPHwW/aLaPjxbU6N5+RXNiwusRD4uDm6/VXHVFarHJvLwY3X48nKrGvxPHawOnzrw2NXhe6qYGMVddyjGjLZ607+z2Fp2IevohejZ+kQM4KJh1wPwad4b7KnKD3JEQgghWsQVBTYn+I/Sw9jfaG13RXVuXO1IknnRLdg3vgGmDzN+OGbfiYeer/VW8/rG+WhtktnnZCb3O6dN76O1Zs1azf88otmYrTEMOOsMxb//XDF0SM9N4g9qvjq8PuHq8D2V06m4/lq46AKFUrByleapZzQ1NZLQC9GTTe53DiMTJxHQfl7PfoxGf32wQxJCCHEcZvxQzNghqLoDzY5SqboyzLghVpu6bkqSedHlGfuzse3fCMqGb8w1h9a8mNrkzex/Ue2pID48mRnpt7RpLaPPp1n4Krzxlqahwarefs9diosvUjgcvSd5PbI6fFGR2erq8D2RUoqzz1LMuVERGmIVBPznY5rdeyShF6KnUkoxY+QtRIfEU9FQwrs5z6OP/GIohBCia1EGvnGzICQSVbkbvHVgBsBbZ/05JBLf2FndtvgdgNLy24iKiop2PV5MTEy7H7PXCnhxff6fqPoy/EPPw59xxaFNn+9YxPL8d3DYnNw5+U8kufu1+m3q660p9TsLwGaD889VnD4F7Pb2SVxNU7dbpfXOcjBmiABqukXMna20VPPCS1aHA4cDrpyhGD9O/o66Krk2i7baVZnH02v+gtYmM0beyoS+ZwYlDjmXRU8h57LoDEZRFo6shRjlO6w19DYnZtwQfGNnYaaMa7f3ae/zOSYm5rj7SAE80aXZt32Kqi9Dh8bgH3HJoefzyrL5In8xANPS57Qpka+o0Dz7gqakBEJC4MbZ7TulPnuTtba6sNCqFO90WOvSr5xBlx7lPlgdPibGQUVF140zmBISFHffBa+9ATlbNa+9oSnaBxdfKDc+hOiJBkSncd6Qq/h0+xu8v/Ul+kcNJdHdN9hhCSGEOAYzZRye5DEYB7Zbxe5cUdbU+m48In9Q9/8EosdStSXY8z4BwJdx1aFKlDWeSt7M/hcazaS+ZzM2eUqr32NvkeaxJ6xEPioSfnZ7+yfy8x/XbMsDtxuS+1iPeXkw/3FN9qZePzGm2wsNVdx0A0w92zpvvvxK8/yLmvp6+bcVoic6PfVShsRm4De9vJ49H1/AG+yQhBBCHI8yMBOGYfabZLWh6wGJPEgyL7oqrXFsfANMP2bCiENTYLTWvLP5WUqrarA3DmCEcxam2bqkKS9P88RTmupqSEqy1oMnJ7dfIm+a1oh8TS30TbFavRmG9ZiSArW1sOgd3er4RddhGIoLz1fMuk7hcEDuNvjn45riEvm3FaKnUUoxM+NO3M4oimv3sCT35WCHJIQQopeSZF50Sca+DRglm62id6OvPVT07u1vV/D+d1ls32Zj6/t38Le/OfjP/zrxEe51WZrnXtB4PDB4ENx1hyI6un2nRe8sgMJCiIs9FP4hSkFMrFU8zVqXLnqCMaMVd92piImBsjJr9sXmLZLQC9HTuF1RXJVxJwCr9y4ne//KIEckhBCiN5JkXnQ9fg+O7Det/007Dx2RBMC3WaUs+O5l6uogtuYq+sX0O+Ep61prln9hrW0OmFbydevcjukdX1NjrZF3uZrf7nJa22tq2v2tRRD1TVHcc5di8CDweODFBZrPl2mpfC1EDzM0LoMzUi8F4N2c56hoKA1yREIIIXobSeZFl2Pf9jGqoRwdGot/2EUABAImT335JD6zkWhjGIm+C094yrppaha/Bx99Yu1zxulWz/D2qlh/pIgIq9idx9P8do/X2h4R0SFvL4LI7bZuEp16inVuffKZ5uVXwOORhF6InuScIVfSP2oojf563sh+jIDpD3ZIQgghehFJ5kWXomqKsed9BoBv9NVgdwKweO0nHPBtxWUPIanyNtRPTt2WTFn3ejUvvQzffa9RCi6/VHHpxapNfemPZ1CqVbW+vByOHJTVGirKIXWgtZ/oeex2xfTLFVddobDZrGKIjz+hKS+XhF6InsJm2Lk68y5C7GHsrtrB0h1vBTskIYQQvYgk86Lr0BrHxtdABwgkZWD2GQ1ASe1evtrzBqaGhJrrcASSmrz0WFPW6+o0Tz2j2ZKjsdth1nWKKad1fNsww1BcOUPhdkNREdQ3QCBgPRYVWSPyV8xQ0sKsh5s8SXHHbYoIN+zbD48+ptm+QxJ6IXqKmNAEZoy8FYCvCj4k78DGIEckhBCit5BkXnQZRtE6jNKtYNjxZ84EpQiYft7a/CTK5iekfjTOirOafe3RpqyXlVmt53bttqbk3zpXMTqz85LnzAzF3Xcp0tKgrhb2F1uPw9Ks6vlduc+8aD+pAxX3zFP07wf19fDMc5qvv5V19EL0FKOSJjG53zkAvLX5Sao9FUGOSAghRG9gD3YAQgDga8SRbU1P9KddgHYnAvDFzvcoqt5JXGQ4YbZbKChXhKYcXh3+4JT1YWmHT1nfs8eqWF9bB9HRcMscRVJi5yfPmRmKUSOtJQA1NdYNh0GpyIh8LxMdrbjzdnj7Haubwnvva4qKFFdM1x1Wt0EI0XkuGnY9hZXbKK7dzVvZT3DzhF9h9JA+xkIIIbom+S0jugR77oeoxkp0WDz+YecDsKcqny92vgvA5ek3ce302BZPWd+aq3niaSuRT0mGu38WnET+IMNQDBmsGDvGepREvndyOBTXzOSHeg2wZq3miac01dUyQi9Ed+ewObkmcx4Om5P8ii18ufP9YIckhBCih5NkXgSdqi7CvmM58EPRO5sTX8DLW5ufQGuTzKSTGN3nlBZPWV+9RvPCSxqvF9KGwp23KyIjJXkWXYNSijNOV9w6RxEaCrt2W+voS0sloReiu0t09+XS4TcB8Hn+IgortwU5IiGEED2ZTLMXwaU1jg0/FL3rMxqzTyYAn21/kwN1+3A7o7gs/eZDux9ryrrWmqXL4LOlVlI0fpziqis6rvWcEG2Rlqa4Zx688JKmpASeflbzszsgJkbOVyG6s/Epp7OjfBMb93/HW5ue5J6T/wunPSTYYQkhhOiBZGReBJWxZzVGWR4YDvyjrwYgvzyHb3d9DMCMUbcS5nAf/ppmpqwHApq33/kxkT/7LGs6syTyoiuLj1PceZsiMREqq+CpZ2XKvRDdnVKKy9NvJiokloqGEj7d/mawQxJCCNFDSTIvgsdXj2PT2wD4h1+EDouj0V/Pos1PATCx71kMjx973MN4PJoXF8Cq1VYP+RnTFBdd0LE95IVoL2634ra5itgYKCuzEvq6OknohejOQuxhTE+/BYDvd39KQcXWIEckhBCiJ5JkXgSNPecDlKca7U7CP/RcAD7KfYXKxgPEhCZw0bDrj3uM2lqrh/zWXI3DATfOVpxysiTxonuJilLcfqsiMhJKSqzWdQ0NktAL0Z2lxY9mQsqZACza/AzegCfIEQkhhOhpJJkXQaGq9mDP/wI4WPTOwdbSdawtWoFCceWo23HZQ495jAMHNI/9S7N7D4SFwe23KEaNlERedE+xsYrbblGEh8PeInj+RY3HIwm9EN3ZhcOuI9IVQ3lDMUu3vxXscIQQQvQwksyLzqc1jg2vAppAynjMxJHUeqt5Z8uzAJw68EJSY0Yc8xC7dmkee0JTVg6xMTDvTsXAgZLIi+4tKVFx61xFaAgUFMJLL2t8PknoheiuQh3hTBs5F4Dvdn0i1e2FEEK0K0nmRaez7f4eozwfbC58mVehtea9nBeo81aTGN6Xc4dcdczX796jf1hXDP36Wm3pEhIkkRc9Q98UxdybFU4n5G2Hha+C3y8JvRDd1fD4sYxLOR2N5p3Nz+ALeIMdkhBCiB5CknnRubx12De9A4BvxMUQGsOG/d+ypWQ1hrJxVcbPcNicR335gTLN8y9YPeSHDoE7blNEREgiL7on09TsyNes32A9mqaVtA8cqLj5RoXdDltyNG+8xaFtQoju5+Jhs4hwRXOgfh9Ld8h0eyGEEO1D+syLTmXPeQ/lrUFH9CEwZCpVjWV8sPUlAM4ePJ2UyIFHfW1trebZ5zW1dZCSAjfdoHC5JJEX3VP2Js3b72gKC8HrA6cDBg6EK2dAZoZi6BDFDbPgpQVWsu9yKq6YoaVLgxDdUKgjnGnpc3h5/SN8W/gxoxInMSA6LdhhCSGE6OZkZF50GlVRiH3nVwD4Rl+LVjYWbX6GRn89/SIHc0bqZUd9rdereeElTVkZxMTA3JskkRfdV/YmzfzHNdvywO2G5D7WY14ezH9ck73JGoVPH6G49hqFUrBytebDJaC1jNAL0R2NSBjP2OTT0GgWbX5aptsLIYRoM0nmRcfRJkbpNow9qzFKcnFsfA3QBPpOxEwYzqo9n7OjfBN2w8lVGXdiM2zNHiYQ0LzyGuzaDaGhMPdmRWSkJPKiezJNa0S+phb6pljntGFYjykpUFsLi975ccr9mNGKq66wzvcvv9Z89nkwoxdCtMXFw2fjdkZxoH4fy/IXBTscIYQQ3ZxMsxcdwijKwpG1EKN8BwS8EPCi/F7M2EH4Mq7kQP1+Ps57FYAL0q4hPjy52eNorXn3fWvdsN0Oc25SJCVKIi+6r50FUFgIcbFw5Ix5pSAm1qpkv7MAhgy2np80UeHxwnvva5Z+rnG54MzT5edAiK7K1CYFFbnUeitxO6NJjRmOoQzCHG6mpc9l4YZH+LpgCaMSJ9EvakiwwxVCCNFNSTIv2p1RlIVrxUPQWIUOTwDDhlGai/I3ompLoHwHb+/9AF/Ay+CYkZzc/7yjHmvZF/D9So1ScN01ilRpPye6uZoaa428y9X8dpcTKnzWfj815VSFxwOffKr5cInG5YSTTzr6z4NpanYWWMeJiIBBqWAY8vMjREfbXLyaxTnPsasyD1/Ag8PmYkB0GtPT5zIqaRLpieMZ3ecUNu7/jrc3P8VdJ/35mIVfhRBCiKORZF60L23iyFpoJfLRA0ApVNUeUAodGg0avl35N3ZHuHDZw7hi1G1HLei1dp3mk0+tqcaXXarIzJBERHR/ERFWsTuPx5pafySP19oeEdF029SzwOtRLF+heeddjdMJ48c1/bk4XnE9IUTH2Fy8midWPUCtt4rY0ERc9hA8/ka2l2XzxKoHuHPyA4xKmsSlI25kR/lmSuuKWJ6/mPPTrg526EIIIbohWTMv2pVxYDtG+Q5rRF4p8NajGioAMCP7sjcsjKVVm8BbzyXDbyA6NL7Z4+Tlad5620rkzzxDMeVUSUBEzzAo1Uqsy8vhyFp2WkNFOaQOtPY7klKKCy+AU09WaA1vvKXZtPnwg7S0uJ4Qon2Z2mRxznPUeqtIiUgl1BGOoWyEOsJJiUil1lvFuznPY2rzh+n2cwD4qvBD9lTlBzl6IYQQ3ZEk86J9eaqsNfJ2F2iNUb0XAB0Sjd8Ryuv+vQS0yciIwYxLntLsIfYWaV5aqAmYMHaM4uILO/MDCNGxDENx5QyF2w1FRVDfAIGA9VhUZI3IXzFDHXVKvFKKaZfDxPEK04SFr2q25VkJ+okW1xNCtJ+Cilx2VeYRG5rYZMaZUorY0EQKK7dRUJELwMjEiWT2ORmtTd7Z8jR+0xeMsIUQQnRjksyL9uWKApsT/B50fRn13hoqtZ/8sDA+8exmX6CGcOVg+tDrm51eX1Ghee4FjccDQ4bA1VchfbVFj5OZobj7LkVaGtTVwv5i63FYGsz72fGXlCiluPIK6ziBALy4QJO/U59QcT0hRPuq9VbiC3hw2UOa3e6yheALeKj1Vh567tLhNxLujKS4dg9f5L/bSZEKIYToKWTNvGhXZvxQzNgh5OxfhW4sxzT9fOoKsK7+AHXaT7K2cUPsFMKSxzZ5bX295tnnNTU10CcJbpylsNslkRc9U2aGYtRIWl2kzmZTXHeNxuuF3G3w/Iua009rXXE9IUTbuZ3ROGwuPP5GQh3hTbZ7Ao04bC7czuhDz4U7I7hsxE28tvGfrCh4n5GJE0mJTO28oIUQQnRrMjIv2pcyyEodR5avGH/AS7UBRQ4XtaaXOu2lFD++tPNBHX7q+XyaFxZoSkohOsrqJR8aKom86NkMQzFksGLsGOvxRKvN2+2KG2crBg+yCup9uhQU1v8351jF9YQQbZMaM5wB0WmUN5SgjyiIobWmvKGEgdHDSI0Zfti2jKTJZCRNRmuTtzc/RcD0d2bYQgghujFJ5kW7MrXJ53s/J910EGI4WOtUlOpaTDRuw4U9LJ7Fpd9gavPQa7TWvPYGFBRAaIiVyEdHSyIvREs4HIo5Nyn69wdDQaMHSkpPvLieEKJtDGUwPX0ubmcURTUFNPjqMM0ADb46imoKiHBGMy19DoZq+tXr0hE3EeaIoLh2N1/sfC8I0QshhOiOJJkX7aqgIpe0vZsIsYWyKzqJnIT+lDtcaKebPgmjiYsceFgBIK01739oVeC22eCG2Yo+fSSRF+JEuFyKW25WJCdDQjzU1cHu3SdeXE8I0TajkiZx5+QHGBqXSa23mv21u6n1VpMWN5o7Jv+RUUmTmn2d2xnJZSNuAmDFzvcoqi5sdj9Tm+SX57Buz1fkl+ccdmNcCCFE7yNr5kW70vs3MKixEexhfB0dy77aHWBzEBOaQLgzEtMMUBEoPVQA6Muv4etvrCHEa2Yqhg6RJEOI1ggLU9w6F554yvp5Kq+A6ioImNbU+mFpViIvfeaF6FijkiaRnjiBgopcar2VuJ3RpMYMb3ZE/qcykiaTXTyJLSWrWbTlKX42+U/YjB+/pm0uXs3inOfYVZmHiR8DOwOi05iePveoNwmEEEL0bJLMi/ZjBuhbsJJdysb68FC2e0vxmz6cNhcJ4SnA4QWA1m/QfLjESjwuudhaNyyEaL2ICMVtt8DjT2oi3BAWDheeDwkJ6oSK6wkh2sZQBoNj00/oNUopLh9xEzsrcthfs4svCz7g7MHTASuRf2LVA9R6q4gNTcQdEkFtYw3by7J5YtUD3Dn5AUnohRCiF5Jp9qLd2Aq+JsrTgOGK5AOjhqrGMgCSIwZgKOOwAkCBiuG8/qaVyJ92quKM5lvOCyFOUHS04vZbFRGRUF8PG7JPrEq+ECJ43K4oLhtxIwDL8xezv2YXpjZZnPMctd4qUiJSCXWEYxg2Qh3hpESkUuut4t2c52XKvRBC9EKSzIv24a3DnvM+Sikco6+n2vTQ6K8n3BGByxZ6WAGgKfF38PLL1lrezAzFZZdIL3kh2lN8nOLWOQqnE3bssKrcCyG6h8ykk0lPmICpAyza/DQ7yrawqzKP2NDEJr8rlVLEhiYeVotGCCFE7yHJvGgX9twlKF8dZkQyO6MT6RuZSpQrDgIuCsp2U15TxdDYTGaN+E++/iCThkarqva1V8uIoRAdITlZcdUV1s/WsuWaLTn6OK8QQnQFSikuT7+ZUHs4RTUFfLf7Y3wBDy57SLP7u2wh+AKeQ7VohBBC9B6yZl60marZjz1/BQDFaVP5Iu95tDeahIL7Kd0bi48qFFE0xA7nfdPA64PEBLj5RoXDIYm8EB1l7BhF4S745lvN629o7r0b4uLkZ06Iri7CFc0lI2bz1qYn2bj/e0Dh8TcS6ghvsu9Pa9EIIYToXWRkXrSZfdPboAP4E0exuPQbyqu87NsyiuLNpxJrpDMw9GRiVDrr1hmsWWdNr79ljiIsTJIKITraJRfBgP7Q0AgLXtH4fDJCL0R3MKbPaYxIGIfLHoKpA5TVF6P14T+/P61FkxozPEiRCiGECBZJ5kWbGMWbsRVvAmUjq89gtpdtonifHaPgJvqlKEJDQSmoqASbAWYA7HaIigp25EL0Dna7Yvb1ivBwq9f8u+8HOyIhREtY0+3nEGoPJyokFo2mqKaABl8dATNwWC2aaelzjtv6TgghRM8jV37RemYAx6a3AKgeeDIf7l5CfQOw63IS3UkcrNNTWgrV1VZS368/lJTAzoKgRS1ErxMdrbj+GoVSsGq1ZvUaGZ0XojuIdMVwyfAbiHBFExuaSL+oIdR6q9lXXUitt5q0uNHcMfmP0pZOCCF6KVkzL1rNVvAVqmY/2hHOR/Zaar1VuFUytuKLcSVZ+5RXQFm59f99kiHCDfuLoaYmeHEL0RulpSnOPxc++Uyz+D1NSgr0TZGlLkJ0dWOTT2NT8UpyD6wnJSKVm8bdhy0EtMdGasxwGZEXQoheTH4DiNbx1mHP+QCA/AHjWb3vGwDOS52Dy+7A44GaWigutnZPiIfoKPB4wemAiIhgBS5E7zX1bBgxXOHzwcsLNQ0NMkIvRFd3cLp9iD2MvdX5FNUUML7f6QyOTZdEXgghejn5LSBaxb71Q5SvDq+7D4tqsgEYn3IGUzJGMHCgNbW+qMjaNzoa4uJAa6got1rSDUoNWuhC9FpKKa69GmJirBkzr79Jk4JaQoiuJyoklouGzQJg2Y5FFNfsCXJEQgghugJJ5sUJUzX7se/8EoAvE5IorttDqMPNBWnXYhiKGdOgsRHq66xidwnxViXtoiJrRP6KGUp6ywsRJGFhihuuV9hssCVH88WKYEckhGiJ8SmnkxY3Gr/p45W1/8TUZrBDEkIIEWSSzIsTdrAV3YH4wSw9sBqAi4ZdT7jTmjtfWqpIToaISHCHQ3EJ1NXCsDSY9zNFZoYk8kIEU79+immXWT+HH3+q2ZEvo/NCdHVKKaaPnIvLHkphxTbW7P0i2CEJIYQIMimAJ07IwVZ0GoN3HPX4G7ykxoxgXPIUAHYWaD77XBMRAbfMgZgYRU2NNSI/KBUZkReiizhpMuzapVizTrPwFc0v7oXISPn5FKIriwqJ49whV/Fp/mt8lvcGoxInHbqRLoQQoveRkXnRcqb/UCu69YkDyK3ejk3ZmZY+B6UU9fWaV1/TaA3jxykmTjAYMlgxdoxiyGCZWi9EV6KUYvo0SO4DtXXw8qsav19G6IXo6ib3O4eUqFQa/HV8mvd6sMMRQggRRJLMixaz7bRa0TU4QnjPWwjAGYMuJSE8Ba01b78DlVVWsbsZ04IcrBDiuJxOxQ2zFCEhUFAAH38S7IiEEMdjM2zMHHM7AGuLVrC7anuQIxJCCBEsksyLlvHWYd9qtaL7KDqCGl8NsaFJnJF6GQDfr4LsTRqbDWZdq3C5ZBReiO4gPl5x9VXWz+uXX2s2ZsvovBBd3aC4dMalnA7AezkvSDE8IYTopSSZFy1itaKrpzA0nO/qdwJwefrNOGxO9u/XfPChlQBcdIGiXz9J5IXoTjJGKc48w/q5ffNtTWmpJPRCdHUXpF1LiD2MfTWFrNq9NNjhCCGECAJJ5sVxqZp92HeuIKA1bzsb0MCYPqcyNC4Dr1ez8DWNzwfDh8HpU4IdrRCiNS48HwYPAo8HFizUeDyS0AvRlbmdkZw3dCYAS3e8Ta2nKsgRCSGE6GySzPd22sQo3YaxZzVG6TZoZqqeI/tt0CZfu10U+asJsYdx0bDrAXj/QygutqrVXzNToZSMygvRHdlsiuuvVUREwP5ieOdd0FoSeiG6skn9ppISOYhGfz0f570W7HCEEEJ0MknmezGjKAvXkvtxffwrXEsfsB6X3I9RlPXjPsWbMUo2U6n9fKLLAWtqn9sVRfYmzcpVGqXg2pkKt1sSeSG6s8hIxaxrFYYB67I0368KdkRCiGMxlMHlI25CoVi/72sKKrYGOyQhhBCdSJL5XsooysK14iGMki1oVyQ6si/aFYlRkmM9X5RltaLLfhOAd8LBq2BAdBoT+55FRYXmrbetUbuzzlCkpUkiL0RPMHiw4qILrZ/n997X7N4jo/NCdGX9ooYwoe+ZALy/9SUCZiDIEQkhhOgsksz3RtrEkbUQGqsIRPVnh+FjfaCCHYaPQFQ/aKzGsX4htvwVqNpiNtHIZl2HoWxMS5+DacKrr2saGmFAfzjv3GB/ICFEezpjilUULxCw1s/X10tCL0RXdn7aNYQ63BTX7ub73Z8FOxwhhBCdxB7sAETnMw5sxyjfwcYQO2/Xr6UwUINXB3AqGwNtEVwZksLo0m04GqvxKFjsagDDwZSBF5Hk7s8nn2oKCiEkBK6/VmG3y6i8ED2JUoqZV2r27YeyMuvm3dybkZoYQnRRYQ435w+9mndznuPzHW+T2eckIl0xwQ5LCCFEB+uyI/Pvvvsuf/jDH7jiiivIyMhg+PDhLFq06Kj719bW8uCDD3L22WeTkZHB2WefzYMPPkhtbW0nRt1NeKrI9pcz35vHNn8lbuUg2QjHrRzk+SuZ782jqGE/ylPDJ0YtlQ470SHxnDV4OjvyNcu+sEbprpyhiI2VL/dC9EShoYobZykcDsjdBp8vC3ZEQohjmdj3LPpFDsYbaOSjba8EOxwhhBCdoMsm8//3f//H66+/TlFREYmJicfct76+ntmzZ/PCCy8waNAgbr75ZoYMGcILL7zA7Nmzqa+v76SouwfTGcHbHKDG9NDXCCdU2TGUIlTZSTHCcfk91Ppq2IOXr+0eQHF5+s34Gp28+rpGa5g0UTFmtCTyQvRkycmKK6ZbP+effa7J3SbT7YXoqpRSXJZ+MwpF9v7vyS/fEuyQhBBCdLAum8z/5S9/YdmyZXz//fdce+21x9z3mWeeIScnh1tvvZXnnnuO//iP/+CZZ55h3rx55OTk8Mwzz3RS1N1Dvi3ATptJnDZQQCAAfr/1qLTmkkY7dWgWOmoxnWFkJE0mLW40b74N1dWQmACXXxrsTyGE6AwTxitOmqzQ2ppuX1EpCb0QXVXfyEFM6jcVgPe3vkjA9Ac5IiGEEB2pyybzp556Kn379j3uflpr3nzzTcLCwpg3b95h2+644w6ioqJ46623pF/yT9T6qvG63Ni1E199Aw11furrNA11fvrUNjIgYCPbYZLvAJc9lIuHz+ab72BLjsZmg+uvU7hcMiovRG9x+aXQry/U18PLr2j8frmeCtFVnTd0JuHOSErrivh21yfBDkcIIUQH6rLJfEsVFBRQUlLC+PHjCQsLO2yby+Vi4sSJFBcXU1hYGKQIux63Mxq/jmR7XV9q/RHYjQAh9kacKsAkn4tS08nHzgCGPYzzhs6kpiyaD5dYX94vuViRkiyJvBC9icOhuGGWIjQUdu+GDz4MdkRCiKMJdYRzQZo1o3F5/jtUNZYFOSIhhBAdpdsn8weT9NTU1Ga3Dxw48LD9BAyIGob3QBrVqoE95gj2+Eew1z+MPr6+hOgwljnqKPa7GRKXwZiEqbzyqiYQgJHpitNOCXb0QohgiIlRXHe1dSPv2+81WetldF6Irmpc8hQGRKfhDXhYkivF8IQQoqfq9q3pampqAHC73c1uP/j8wf2aExUVhWG0732NmJiu2xJmW54f+85bcQ16AJ9jFwQScZvhnKZL2GWrZp0DdP1Azkz+OZ8vC6eyykdCgsHtt4YTHi6j8r1RVz6fRec55RQ4UObho088vP+hYszocOLiutc9YTmXRU9xvHN59kn38j/Lfsm2iiyKvYWMSBrbOYEJcYLkuix6ks4+n7t9Mt8eqqqq2vV4MTExVFRUtOsx29PevRp9YDx93f+PAzHP0+jM43SqsOHjc4fG4x2Ba9/VbF0Ty6o19SgFV0xXeL0+vN5gRy86W1c/n0XnOvUUzabNmvyd8OzzXm6dq7pN/3k5l0VP0ZJzOYxoJiSfzXe7PuGV1fO555S/YjccnRShEC0j12XRk7T3+dySGwPda0ilGREREQBH7Sd/8PmD+wmIiACnA+xVk0gtns/4kv/HmfVpbCaO/f4R2BsHE1ZyBd98Z+1/ztmKIYO7x5d1IUTHMgzFVVdY/efztsOatcGOSAhxNFMHz8DtjKKsfj9fFywJdjhCCCHaWbdP5g+uiS8oKGh2+8G18gf3EzAoFQYOhPJyQBucY9tADQ6+dQRo1G6MnTdiBkJw2CE1Fc6ZGuSAhRBdSny84vxzrRt8H3yoqa6W9fNCdEWhjnAuHHYdAF/sfI+KhtIgRySEEKI9dftkPjU1lcTERNatW0d9ff1h2zweD2vWrCExMVGS+Z8wDMWVMxRuN7gqckg2tvB5aAGlviQCJRMwKsYTFQmhoXDdNQqbTUblhRCHm3Ka1a6uoRHefT/Y0QghjmZMn1NJjRmB3/SyJHdhsMMRQgjRjrp9Mq+UYubMmdTX1/PYY48dtu3JJ5+kqqqKmTNndps1nZ0lM0Nx98/gysGLybaXsMV04PFGkhqYTWysNRX/qisVMdHy9yaEaMpmU8y8UmEzIHuTZmO2jM4L0RUppbhsxE0oZZBTupbcA+uDHZIQQoh20mUL4L355pusXWstxty2bduh51atWgXAueeey7nnngvArbfeyrJly3jmmWfIyclh1KhRbN26lS+//JL09HRuvfXW4HyILm5s9Bq8aTv5oGYvUeFDuCDpCraviKMWOPkkRWaGJPJCiKNLTlacfRYsXaZZ/K5m6BAIC5PrhhBdTZK7H6cOuIBvCj/ig60LGHzKSBw2Z7DDEkII0UZdNplfu3Yt77zzzmHPrVu3jnXr1gHQt2/fQ8l8WFgYCxYsYP78+XzyySesWrWK+Ph4br75Zu6++27CwsI6Pf4uL+DFvuVdlnj34I2MYWhSKnVbz6O2DpKS4LJLgh2gEKI7mHo2bNwEJSXwwYdw9cxgRySEaM7UwTPYuP87KhpK+KrgQ6YOmRHskIQQQrSR0lr3+rmR7d0Sozu02bBtX0r5hgU84tmCP34YmcavWfv5KBwOuGeeok+SjK4JS3c4n0Vw7dqleewJjdYw92bFiOFd8/oh57LoKVp7LmfvX8nr2fOxGw7uOeVB4sKSOiA6IVpOrsuiJ5HWdKJz+Oqx537Mh95CAu5Ekl3jWf/FKAAuu0QSeSHEiRkwQDHlVOu6sWixxuPp9feIheiSMpImMyQ2A7/p48PcBch4jhBCdG+SzPdC9m2fsq1hL1tpgJB4ytdeSyBgFcU7aXKwoxNCdEfnnwexMVBZCR99HOxohBDNUUpx6YgbsCk72w5sYGtpFgCmNskvz2Hj/u/IL8/B1GaQIxVCCNESXXbNvOggDRWo7Ut531OAGZVMVP15lJYmExkJV85Aqv4LIVrF5VJceQU8/azm2+81o0fD4EFyPRGiq0kIT+G0gRfyZcEHfJi7AK+/kQ+2LWBXZR6+gAeHzcWA6DSmp89lVNKkYIcrhBDiGGRkvpdx5LzPKs9eSuwGhr0PpWunAdb0eqlCLYRoi7ShikkTrevI24s0Pp9M4RWiKzpr0DSiQmLZXbWdh7/5d7aXZeN2RtInoj9uZyTby7J5YtUDbC5eHexQhRBCHIMk872IqtqLp/BrPvHuxnT3wdhzBfjDGT4MRmcGOzohRHdgmpod+Zr1G6xH0zw8Yb/0YoiMhNIDsPTzIAUphDgmpz2EC9OuZ3/tbioaSokP60OoIxxD2Qh1hJMSkUqtt4p3c56XKfdCCNGFyTT7XsS+ZTGfenZT5wpHBYZSt+NsnA6YfrmS6fVCiOPK3qR5+x1NYSF4feB0wMCB1hKdzAzrGhIaqpgxDV5coFnxlWZ0JvTtK9cXIbqaMEcEphnAYXNSUreXfpFDDn0XUEoRG5pIYeU2CipyGRybHuRohRBCNEdG5nsJo3Qb5ftW862vGH9YEo0516Gwcc5URVycfNEWQhxb9ibN/Mc12/LA7YbkPtZjXh7Mf1yTvenHEfpRIxWjMxWmCW8u0vj9Mt1eiK6mzldFqMONTdmo89ZQ6606bLvLFoIv4KHWWxmcAIUQQhyXJPO9gdbYNy/iQ08h/rAYdNVkVOVoEhPhjCnBDk4I0dWZpjUiX1MLfVMgNBQMw3pMSYHaWlj0zuFT7qdfDmFhUFQEK74KYvBCiGa5ndGEOsKJcFl9jEtq92LqwKHtnkAjDpsLtzM6SBEKIYQ4HknmewGjaB07DmxgS6CKBnsfGnOuBeCK6Qq7XUblhRDHtrMACgshLhaOXJGjFMTEQkGhtd9Bbrfi8kutnZd+rikpkdF5IbqS1JjhDIhOw9QmDsOJz/RyoL4YAK015Q0lDIweRmrM8CBHKoQQ4mgkme/pTD/GpkW87ynEDEvAv+cCnP4UJk1U0jZKCNEiNTXWGnmXq/ntLqe1vabm8OfHjYURwxWBALy1qGmxPCFE8BjKYHr6XCJc0ShlYOoA5fXFVDWWU1RTQIQzmmnpczCUfFUUQoiuSq7QPZxt51esqcphP15qfAOw7Z1OWBhcfGGwIxNCdBcREVaxO4+n+e0er7U9IuLw55VSXDEdnE5r5P7b7zs8VCHECRiVNIk7Jz/AqKRJ2AwHjf4G9tfuJi1uNHdM/qP0mRdCiC5OkvmezNeAL+ddPvHuwheSiC//SmzazaUXK8LDZVReCNEyg1KtqvXl5aCPGFzXGirKIXWgtd+RoqMVl1xkXW8+/kRTUSGj80J0JaOSJvGbMx/jN2c8xtC4UfSPHMLMjDslkRdCiG5AkvkezJ73GcvqcqkzDCqrRhJePZXBg2DC+GBHJoToTgxDceUMhdttFbSrb4BAwHosKrJG5P9/e3ceHlV5sH/8eyaZAFmACRCWmIDsa9gDCAoCIkJIQVH4gUWqqCz6KlbqAq+21qK1rbZsl0AtVAtI2WQTCYossgWIsm8CCZCwJoQkZJtkzu+PuZKXkCCBBE4m3J9/5so5JzM30zid+zznec7jgwxstqJPEnbuBPXvh+xs9+X25vVnBETEUjbDRrvgB+nd4El8ffxZ+/NC3V9eRMQDqMyXVxnJJB9dxZbsc6R51cJ2ajjeXt4MGqh7yovIrWvV0pLzio0AACciSURBVOClsQaNGsHVNDh33v3YuBGMG2Pk32e+KIZhMPhxA7sdjv0Mu2PuYnARKbaH6w+kgnclzqWe4qezW6yOIyIiN+FtdQC5M+xHVrM642dyvCuRcr4rjqwwuj9sUDNIRV5Ebk+rlgYtmrtXrU9NdY/I31+PG47IX6t6dYM+vWH1GpOVq0waN4LKlfV5JFKW+PkE0P3+SKKOLeTbnxfRsmY4Pl43WPlSREQsp5H5cshIPcvJn7/mQE4Sic46+F/8f1QPNOj1sNXJRMTT2WwGDeobtGntfixOkc/TrSvcFwwZmbB85R0MKSK3rUtIH6pUrEZK1mW2xn1jdRwREfkFKvPlkG3/MlZmncTpVZnMhAh8cu7jV5EGdrtGwUTEOl5eBoOfMPCywb79Jvv2a+68SFlj9/KhT8OnANgUu5K0rCsWJxIRkRtRmS9njMTj/HjmW8660rmQVg9H6iDCWhk0baIiLyLWq1Pb4OEe7s+jr5abpKer0IuUNWG1uhBcuT7ZuVmsP7HM6jgiInIDKvPliWmSu3ch32Sd5iqBeF0Yip9PAJERVgcTEfk/PR+GoCBITYNVq61OIyLXMwyDvo2HArAz/nsupMVbnEhERIqiMl+O2M7+xMYLP5DqyuXS5ZZUudqbvn0MLTIlImWKt7fBU08YGAbsijE5clSj8yJlzf2OZjSt0Q7TdBH180Kr44iISBFU5ssLVw4p+xawKfssl53V8E96htD7vOncyepgIiKFhYYadHvAfaJxyTKTrCwVepGypm+joRiGjcMXf+RE0kGr44iIyHVU5ssJr7itrEnaRVaujZSkh/DLbsPjA29tpWkRkbupzyMQ6IDkZFiz1uo0InK96n616RjsvhXON0cXYJo66SYiUpaozJcHOVmc2vcF+5xJXMisiSNlBA8+YCM4WEVeRMquChUMnnjc/Tm1dZvJyVgVBZGypmeDx/HxqkhCaix7zm2xOo6IiFxDZb4csB2LYmXqQTJyfHAlP06Qbwh9HrE6lYjIzTVqaNCxg7vQL15i4nSq0IuUJf4+lel+/wAA1v28GGdutsWJREQkj8q8p8tKZc/BeZzJucqlq6E4UgcTOcCgQgWNyouIZ4joB5Urw8VL8O16q9OIyPUeCO1LlYqBXMlMZNspzYkRESkrVOY9XO7BZXyTcZy0rErYr4ykZeMqtGxhdSoRkeKrVMlg0K/cJyA3bjKJT9DovEhZYvfy4ZGGTwKwMXYladkpFicSERFQmfdoRtoFNh9bRFK2k5T0FlTP7sPASPf9YUVEPEmL5gZhrQxcLli5ytRCWyJlTOtaXakdUJesnAw2nPjK6jgiIoLKvEdL3TuPjZlnuJIVQKXk0fTpZcfhUJEXEc8U0Q/sdjhxEg7oLlgiZYphGDzWeBgA0WfWc+nqWYsTiYiIyryHMpJO8k3cKq5mmWSnd6N+1bZ062p1KhGR21e1qsGD3dwnJFevMcnJ0ei8SFlSP7A5Taq3wWXmsvbYQqvjiIjc81TmPZFpEh8zmx+zEknOduCX/CJPDLLh7a1ReRHxbA93hwB/SEyE7TusTiMi13u00VAMDA5d3E3s5cNWxxERuaepzHsK04Xt4lFsZ3bidWg1K89vJDPLwEgbxINt61Kvroq8iHi+ChUMHu3j/jxb951JerpG50XKkiD/YDoE9wBgzdEFWt9CRMRC3lYHkJuzJfyIV8x/iEvaR2pOBinZycSSTVp2PWrm/Jp+fa1OKCJSejq0hy1b4ew5+G49DIiwOpGIXKtng8fZc24r8Skn2Hd+O2G1ulgdSUTknqSR+TLOlvAjR9dP5P0LX/OHnJOsdJ7lK1JIzbHjjOvFoPCL+PpqVF5Eyg+bzSCin/tzbes2k0uXNPInUpYEVKjKg/XcZ9mijv0XZ262xYlERO5NKvNlmeni8I6/MyXzMPtynPhk2Al0mqTlViD5ah2Ssyrgm/YnMF1WJxURKVWNGhk0bWKQ64Kvv7E6jYhcr2vdxwioUJXkzEvsOP2t1XFERO5JKvNl2cWjLErcRVIuBGb70tplcNDLJDu3IpfjIvFp8CVLEnfBxaNWJxURKXX9+4HNBvsPmJw4odF5kbLEx6sCjzR8CoANJ5eT7kyzOJGIyL1HZb4MO3lpH7GudAJyKuFngwxbJtmuClxNaUxgwGkcPimcdKVz8tI+q6OKiJS6mkEGncLdl9uvXG1qoS2RMqZN7a7U9A8hMyed7098ZXUcEZF7jsp8GXYi0STLNKlogI+Zy3HDIDOnIlfie1It5FsqGpBlmpxI1BdcESmfHukFFStCfAL8+JPVaUTkWjbDxmONhwGw4/S3JKaftziRiMi9RWW+DMv2bg6uSpi2TC44Azl9uRXnTkVQK+R7bF45uGyZmK5K7uNERMohf3+Dnj3co/NrvjHJztbJS5GypGG1ljSqFobLzCXq2H+tjiMick9RmS/DGgY1I/dqGEmGi0pkkXv0N1Q2vAlwHMJupJNkuHBdDaNhUDOro4qI3DFdHwCHA66kwKbNVqcRkev1bTwUA4MDF6KJS9Y6PiIid4vKfBnW4H4bNXNfIzWzLucqJRPa+XXqNJyH08jmtGGQllmXWrmv0eB+/c8oIuWX3W7Qr697dH7DJpOUFI3Oi5QlNf1DaBf8EADfHJ2v9S1ERO4StcAyzGYz+E3/cCqf+TMpSQ9zyhXEMSoTlxtESlJPKsf/mZH9w7HZdJ95ESnfwlpBaAhkZ8PadVanEZHr9WrwBHYvH05fOc7+89FWxxERuSeozJdxrVoavP5MOO1zpuN3ZCbeB6fgd2QmHXKm8fqIcFq1VJEXkfLPMAwiI9yfd7t2mySc1cifSFlSuYKDB+tGABD183/JcTktTiQiUv55Wx1Abq5VS4MWzb04Gduc1FQICID766EReRG5p4SGGrQOgz17TVauNnnhOXfJF5GyoVvdx9gZv57LGRfYcfo7utbta3UkEZFyTSPzHsJmM2hQ36BNa/ejiryI3Iv69QVvbzh+HA4fsTqNiFzLx7sivRo8AcCGE1+R4bxqcSIRkfJNZV5ERDyGw2HwYFf3yczVX5vk5Ohye5GypF2dh6jpfx8ZOVfZcHK51XFERMo1lXkREfEoPbqDnx9cuAjRO61OIyLXshk2Hm00FIDtp9aRlH7B4kQiIuWXyryIiHiUSpUM+vR2j85HfWuSkaHReZGypFG1MBoEtiTXzCHq54WcSDrE3nPbOJF0CJfpsjqeiEi5oQXwRETE44R3hC3b4MIFWL8B+j9mdSIRyWMYBn0bD+XPG19m2cHPWHvsvxiA3asCoVUbMbDZs7So2dHqmCIiHk8j8yIi4nG8vAwi+rlH53/YYpKYqNF5kbIkKf0CF68mcDU7hcycq9T0vw9/n8r8nLiPT6N/z4HzmiMjIlJSKvMiIuKRmjSGRg0hNxfWrLU6jYjkcZkuvjr0L7xsdirZ/XDmZpPuTKOS3Y86AfVIy77C8kNzdMm9iEgJqcyLiIjlXC6T4ydMftrjfnS5bj7Sbhju0XnDgL37TGLjNDovUhbEXj7CqeRj1PCrTWClmgAkZVwE3P/dBlYKIi75KLGXdX9JEZGS0Jx5ERGx1L79JkuWmcTFQbYTfOxQty48MQhatTR+8Xdr1zYI7wA7dpqsXG3y0hh3WRAR66RlJ+PMzaKCd0UqeFfEmZtFRbtv/v4KXhW5nHuRtOxk60KKiJQDGpkXERHL7NtvMm2GydFj4O8PtWu5H48dg2kzTPbtv/lo+yOPgI8PnD4Ne/behdAi8ov8fapi96pAVk4mNsOLOpXrEVgpKH9/Vm4mdq8K+PtUtS6kiEg5oDIvIiKWcLncI/KpaRBcBypVApvN/VinDqSlwdJlN7/kvnKAwcM93KPxX39j4nTqcnsRK9VzNCG0aiOSMi5gmgX/ezRNk6SMC9St2ph6jiYWJRQRKR9U5kVExBInYyEuDqoFwvVXxhsGOAIhNs593M081A2qVoHkZNi85Q6EFZFisxk2BjZ7Fn+fKiSkxpLhvIrLlUuG8yoJqbEE+FTlV81+g83Q11ARkZLQp6iIiFgiNdU9R75ChaL3V/Bx709Nvflz2e0GfR91nxH4foNJaur/jQbmLa63a7ez2IvriUjJtKjZkdHhv6dhtVakZadwLu00adkpNKoWxovh7+o+8yIipUAL4ImIiCUCAtyL3WVluS+tv15Wtnt/QEDxnq9tG9iyFU6fgahv3QvoXbu4Xm5uGl5eZrEX1xORkmlRsyPNgtoTe/kIadnJ+PtUpZ6jiUbkRURKiT5NRUTEEvfXc69an5QE102rxTThchLUq+s+rjgMw6B/P3dBj95psmGTq8DiesHBtlteXE9ESsZm2Kgf2IywWl2oH9hMRV5EpBTpE1VERCxhsxk8McjA3x8SEiA9A3Jz3Y8JCe4R+ccHGdhsxR9Br3+/QauWBqYLPpvDdYvrGbe8uJ6IiIhIWaUyLyIilmnV0uClsQaNGsHVNDh33v3YuBGMG2Pc1qXwj/WFzCy4cB4qVSz54noiIiIiZZHmzIuIiKVatTRo0dxdrFNT3SPy99fjlkbkr1W9mkHTJiaHDrtXtw8sYrX8Cj5wuZiL64mIiIiURRqZFxERy9lsBg3qG7Rp7X683SKfp/tD4OUFGZnuQn+9W11cT0RERKSsUZkXEZFyp1lTgwb1wZkNFy+55+LnuZ3F9URERETKGpV5EREpd2w2g+efg4qV3HPwz56D3FyzRIvriYiIiJQlKvMiIlIutWlt4/nnwM8fEhPh9OncEi+uJyIiIlJWaAE8EREptwb0Nzh71mTffmjR3Ic+j2SXaHE9ERERkbJCI/MiIlJuGYbBgAiDqlWhVi2vUllcT0RERKQs0Mi8iIiUa3VqG0x6C4KCKpKSkml1HBEREZFSoZF5EREp9ypWNPDy0oi8iIiIlB8q8yIiIiIiIiIeRmVeRERERERExMOozIuIiIiIiIh4GJV5EREREREREQ+jMi8iIiIiIiLiYcpVmd+7dy/PP/88HTt2pE2bNgwePJiVK1daHUtERERERESkVJWb+8zv2LGD5557DrvdTv/+/QkICCAqKorXX3+d+Ph4Ro8ebXVEERERERERkVJRLsp8Tk4OkyZNwjAM5s2bR/PmzQEYN24cQ4cOZerUqfTt25d69epZG1RERERERESkFJSLy+y3b9/OqVOniIiIyC/yAP7+/owdO5acnByWLl1qYUIRERERERGR0lMuynx0dDQA3bp1K7Sva9euBY4RERERERER8XTloszHxsYCULdu3UL7qlSpgsPhIC4u7i6nEhEREREREbkzysWc+bS0NAACAgKK3O/v78+5c+du+PsOh6PUM92J5xSxiv6epbzQ37KUF/pblvJCf8tSntztv+dyMTIvIiIiIiIici8pF2Xe398fgNTU1CL3p6Wl3XDUXkRERERERMTTlIsyn3fLuaLmxV+5coXLly8XOZ9eRERERERExBOVizLfsWNHAH744YdC+7Zs2QJAeHj4Xc0kIiIiIiIicqeUizLfpUsXQkJCWLVqFYcOHcrfnpaWxowZM/D29mbQoEEWJhQREREREREpPYZpmqbVIUrD9u3bGTVqFHa7nYiICPz9/YmKiuLMmTO8+uqrjBkz5o6+/t69e5k6dSo//fQTTqeThg0b8swzzzBgwIA7+roipen8+fOsWbOGTZs2ceLECS5dukSVKlVo164do0aNonXr1lZHFLlts2fP5q9//SsACxcupE2bNtYGErlF69atY/78+Rw8eJCMjAyqV69OmzZtmDBhArVr17Y6nshNmabJunXr+OKLLzh58iSpqanUqlWLTp068fzzzxMSEmJ1RJECli9fzu7du9m/fz9Hjx7F6XTywQcf8Pjjjxd5fFpaGlOnTiUqKoqLFy9So0YN+vTpw8svv5y/zltpKjdlHtyFesqUKYUKdWRk5B193R07dvDcc89ht9vp378/AQEB+ScSxo8fz+jRo+/o64uUlr/+9a/Mnj2b0NBQOnbsSLVq1YiLi+Pbb7/FNE3+9re/0a9fP6tjityy48ePM3DgQLy9vUlPT1eZF49imibvvvsuCxcuJDQ0lG7duuHn58eFCxfYuXMnf/nLX+jQoYPVMUVu6sMPP2TOnDnUqFGDXr164e/vz+HDh9myZQu+vr58+eWXNG7c2OqYIvl69uxJfHw8DocDX19f4uPjb1jm09PTGTZsGIcOHaJr1640b96cw4cPs3nzZpo1a8b8+fPx9fUt1XzlqsxbIScnh8cee4xz586xcOFCmjdvDrjPygwdOpSTJ0+yevXq/EX6RMqyqKgoAgMDC30p3LVrFyNHjsTPz4/Nmzfj4+NjUUKRW5ebm8uQIUMwDIN69eqxYsUKlXnxKJ9//jl/+tOfGD58OBMnTsTLy6vA/pycHLy9vS1KJ1I8Fy9e5KGHHqJ27dqsWLGiwCjl3Llz8wvSBx98YGFKkYK2bt1K3bp1CQ4OZtasWfztb3+7YZmfMmUK06dPZ9SoUUyYMKHQ9nHjxvE///M/pZqvXMyZt9L27ds5deoUERER+UUe3LfLGzt2LDk5OSxdutTChCLF16dPnyJHdzp06ECnTp1ITk7myJEjFiQTuX2zZ8/m8OHDTJ48uVAJEinrMjMzmT59OiEhIbz99ttF/g2ryIsniI+Px+Vy0a5du0KXG/fo0QOApKQkC5KJ3NgDDzxAcHDwTY8zTZNFixbh6+vLuHHjCux78cUXqVKlCosXL6a0x9FV5ksoOjoagG7duhXa17Vr1wLHiHiyvC+L+tIonuTo0aNMmzaNMWPG0KhRI6vjiNyyLVu2kJycTO/evXG5XERFRTFr1iwWLFhQ5C15RcqqunXrYrfbiYmJIS0trcC+jRs3AtC5c2croomUWGxsLBcuXKBdu3aFLqWvUKECHTp04Pz586X+ua1v5SUUGxsLUOR97KtUqYLD4dD/2YrHS0hIYOvWrdSoUUNz2cRj5OTk8Oabb9KgQQNeeOEFq+OI3Jb9+/cD4OXlRWRkJCdPnszfZ7PZGDlyJG+88YZV8USKzeFwMH78eD766CP69etHz5498fPz4+jRo2zbto0hQ4bw9NNPWx1T5Lbk9b0bTa3O64pxcXGlOv1aZb6E8s4sBgQEFLnf39+fc+fO3c1IIqXK6XTyu9/9juzsbF5//XVdpiwe49NPP+XIkSP897//xW63Wx1H5LYkJiYCMGfOHJo3b86iRYto0KABhw4d4n//93/517/+RUhICMOGDbM4qcjNPffccwQFBfHOO++wYMGC/O1t27YlMjJSn9XisVJTUwFuuGJ93va840qLLrMXkRtyuVy8/fbb7Ny5k6eeeoqBAwdaHUmkWA4fPsynn37Ks88+S4sWLayOI3Lb8uZX2u12pk+fTlhYGH5+fnTo0IEpU6Zgs9mYM2eOxSlFimfGjBm89dZbvPjii2zcuJEff/yR+fPnk5uby4gRI4iKirI6oohHUZkvoZudZUlLS7vhqL1IWWaaJpMmTWLFihVERkbyhz/8wepIIsX2xhtvEBISwssvv2x1FJESyfue0bJlS2rWrFlgX6NGjQgJCeHUqVOkpKRYEU+k2LZt28Y//vEPhg8fzujRo6lVqxa+vr60b9+emTNnUqFCBa1kLx4rr+9dvx5EnptdzX27dJl9CeXNeYiLi6Nly5YF9l25coXLly/Ttm1bC5KJ3D6Xy8XEiRNZunQpERERfPjhh9hsOvcnnuPw4cMAtGrVqsj9Q4YMAWD69On07t37ruUSuVX169cHbvwFMG97ZmYmlStXvmu5RG5V3iJ3nTp1KrQvMDCQJk2a8OOPP5KUlERgYODdjidSInlz4vPWU7te3pz6otZZKwmV+RLq2LEjM2fO5IcffqB///4F9m3ZsgWA8PBwK6KJ3JZri3y/fv346KOPNE9ePM7gwYOL3L5r1y5iY2Pp2bMngYGBxbrdjIiV8orPiRMnCu1zOp2cOnUKX19flR8p85xOJ3Dj28/lbffx8blrmURKS7169QgKCiImJob09PQCK9pnZWWxa9cugoKCVObLmi5duhASEsKqVasYMWIEzZo1A9yXUsyYMQNvb28GDRpkcUqR4rm2yPft25e//OUvKvLikf70pz8Vuf3NN98kNjaWF198kTZt2tzdUCK3ITQ0lG7duvHDDz+waNEinnzyyfx9s2bNIiUlhcjISN02VMq8du3a8Z///Ie5c+fy6KOPFrjaZNmyZcTFxdGiRYsbLiAmUpYZhsGTTz7J9OnTmT59OhMmTMjfN3PmTK5cucK4ceMwDKN0X9cs7TvX34O2b9/OqFGjsNvtRERE4O/vT1RUFGfOnOHVV19lzJgxVkcUKZapU6cybdo0fH19GTFiRJFfDnv37p1/0krE07z55pssW7aMhQsXqsyLxzh16hRDhw4lMTGRHj16UL9+fQ4ePMj27dsJDg5m4cKF1KhRw+qYIr8oNzeXkSNHEh0dTWBgID179qRy5cocOXKELVu24OPjw5w5c+jQoYPVUUXyLVq0iN27dwNw9OhRDhw4QLt27fJH2Hv37p0/XS89PZ1hw4Zx6NAhunbtSosWLTh8+DCbNm2iWbNmzJ8/v9A96EtKp3FLQefOnZk/fz5TpkxhzZo1OJ1OGjZsyCuvvEJkZKTV8USKLT4+HnB/GH366adFHhMcHKwyLyJyF4WGhrJkyRKmTJnC5s2b2bJlC9WrV2f48OGMGzeOatWqWR1R5Ka8vLz47LPP+Pe//82aNWtYvXo1TqeTatWqERERwYsvvkjjxo2tjilSwO7du1m2bFmBbTExMcTExADu78V5Zd7X15cvvviCadOmsXbtWqKjo6levTojR47kpZdeKvUiDxqZFxEREREREfE4Wp5aRERERERExMOozIuIiIiIiIh4GJV5EREREREREQ+jMi8iIiIiIiLiYVTmRURERERERDyMyryIiIiIiIiIh1GZFxEREREREfEwKvMiIiIiIiIiHkZlXkRERERERMTDeFsdQEREpCxp0qTJLR0fHBzM+vXr+fWvf010dDTfffcd99133x1Kd+eMGDGC06dPs3btWnx8fKyOU2b17NmT+Ph4jhw5clu/v27dOl566SX+/ve/89hjj5VyOhERuZeozIuIiFxj0KBBhbbt3r2bU6dO0bRpU5o1a1Zgn8PhuFvR7pgNGzawY8cOfv/736vI32G9e/emadOmfPzxx/Tq1Uvvt4iI3DbDNE3T6hAiIiJl2ZtvvsmyZct46aWXePnll4s8JiEhgYyMDEJDQ7Hb7Xc5YclERkZy6dIlNmzYoHJ5EyUdmQdYvXo1r732Gu+++y7Dhg0rxXQiInIv0Zx5ERGRUlCnTh0aNGjgcUV+9+7dHDlyhH79+qnI3yW9evXCz8+PL7/80uooIiLiwXSZvYiISCm40Zz5Jk2aEBwcTFRUFDNnzuSrr77i/PnzBAcHM2rUKJ544gkAtm3bxowZMzhw4ABeXl48/PDDvPXWW0Vexp+dnc2CBQtYsWIFJ06cwDRNGjZsyJAhQxg8eDCGYRQ79+LFiwH36HxR9uzZw6xZszh48CAXL16kSpUq1K5dmy5dujB69Gj8/PwKHL9r1y7mzJlDTEwMqampBAUF0bNnT8aOHUtgYGCh5zdNk5UrV7JkyRIOHz5Meno6QUFBtGnThmHDhtG+ffsCx2/cuJG5c+eyf/9+MjMzqVOnDo888ggvvPAClStXLnDs1KlTmTZtGh988AEtWrTgk08+Yffu3TidTlq2bMlrr71Gu3btCmXKycnhs88+Y8mSJZw9e5agoCAGDBjA2LFjb/g+3sr7VLFiRXr37s3y5cvZs2cPrVu3vuHzioiI3IjKvIiIyF3w6quvsnXrVtq2bUtoaCjR0dG8/fbbAPj5+fHb3/6WJk2a0LVrV/bs2cPy5cs5c+YM8+bNK1DO09PTef7559m1axcOh4P27dtjs9n46aefmDRpEvv27eO9994rdq6NGzfi6+tLy5YtC+3bsGEDY8aMwTAM2rVrR9u2bbly5QqxsbHMmjWLIUOGFCipn3/+OZMnT8ZmsxEWFkZQUBDHjh3jiy++4Pvvv2fBggUEBQXlH5+bm8v48ePzF91r3749DoeDs2fPsm7dOux2e4EyP3PmTD7++GO8vb3p2LEjDoeDmJgYZs+ezbp165g3bx7Vq1cv9O/Yv38/7733HjVr1qRLly7ExcWxc+dORo4cyeLFi2ncuHGB41977TXWrl2Lr68vDz74IKZpMnfuXA4dOkRRsxNv9X0CCA8PZ/ny5WzYsEFlXkREbovKvIiIyB0WHx+Pn58fX3/9NbVq1QJg+/btPPPMM3zyySc4nU4+/vhjHn30UQDS0tIYOnQou3fvZseOHXTu3Dn/uT766CN27drFr371K9599938kpiUlMTo0aNZuHAhPXv2pEePHjfNdfz4cRITE+nYsSM2W+GZd5999hmmabJo0aJCZX/v3r1UrVo1/+effvqJDz74gDp16jBjxgyaNm0KuEfeZ8yYwZQpU3j//feZMmVK/u/MnDmTtWvX0rhxYz799FOCg4Pz9yUnJ3P8+PECr/f3v/8dPz8/5s6dS1hYGOC+SmHChAl88803/PGPf+Qf//hHoX/HvHnzeP3113n++efzt02ePJl///vf/POf/+Sjjz7K375q1SrWrl1LSEgI8+bNo2bNmgCcPn2ap59+mnPnzpXofcqTl3/Xrl2F9omIiBSH5syLiIjcBRMnTswv8gCdO3emRYsWXLx4kR49euQXeQB/f3+eeuopAHbu3Jm/PTExkcWLF3Pffffx/vvvFxjtDQwMzB+RL+5c7LxF3O6///4i9ycmJhIQEFDkqH1YWBj+/v75P8+aNQuXy8V7772XX+QBDMNg7NixNG/enHXr1pGUlAS4S/icOXMwDIPJkycXKPIAVatWLTAqP2/ePFwuF88880x+EQbw8fHhnXfeoWLFikRFRXH+/PlCWdu3b1+gyAOMGTMGKFymFyxYAMArr7ySX+QBQkJCbniZ/a28T3nq168PUKKF9ERE5N6mMi8iInKH2e12wsPDC23Pm1v/wAMPFNoXGhoKwMWLF/O3RUdH43Q6efDBB4tcrK5p06b4+fmxf//+YuXKK9bXzzXP06JFC1JSUnj77bc5evToDZ/H5XKxbds2/Pz86NKlS6H9eZefu1wuDhw4ALgvfU9JSaF58+a0atXqplnzSveAAQMK7atWrRpdu3bF5XIRExNTaH/Xrl0LbXM4HFStWpULFy7kb3M6nezZswebzVbg5Eqe/v37F5mtuO/Ttby9vfHz8yMlJYWcnJxi/Y6IiMi1dJm9iIjIHVajRo0iL2P39fUFKDACnKdSpUqAewQ7T3x8POAePc4bQS5KVlZWsXKlpqYCFJrPnee1117j6NGjLFmyhCVLluBwOGjbti29e/dmwIAB+ScUkpOTSU9PB6B58+a/+JqXL18GyL9cPe+kxc1cuHABwzAKjeDnydt+bTnPc+0VEdfy8/MjOTk5/+fk5GScTic1atQo8mSJv78/lStXJiUlpcD24r5PRT3f1atXSUtLK/JSfBERkV+iMi8iInKH3Wx1+eKuPu9yuQB3YW7SpEmJcwUEBADuOfpFqV27NkuWLGH79u1s2LCB6Ohovv/+e9avX88///lPvvzyS6pUqUJubi7gLsd9+vT5xdesU6dOgZ9vZeX94ijq+Yr7GnmL291qpuK+T9dLTU3FMIwiL8MXERG5GZV5ERERD5E3gh8eHs5bb71V4ufLu1XclStXbniMt7c33bp1o1u3bgAkJCTw1ltvsX37dmbNmsWECRNwOBz4+Phgt9v58MMPi/XaeaPlcXFxxTo+KCiIM2fOEB8fnz/f/FoJCQmA+yqI2+VwOLDb7Vy6dIns7OxCI+ppaWmFRuXzFOd9upbT6SQ9PZ0qVarg7a2vYyIicus0Z15ERMRDdO7cGS8vLzZs2JA/Gl4SeQvVnThxoti/U6dOnfzF5PLmh3t7exMeHk5ycnKBBft+ScuWLalcuTIHDx4s1hz/Dh06ALBy5cpC+5KSktiyZQs2m63I+8YXl91uJywsDJfLRVRUVKH9X3/9dbGfq6j36Vp57/m1iwWKiIjcCpV5ERERD1GzZk0GDRpEbGwsv/vd7/IXsLtWTEwMGzduLNbz1a9fn2rVqnHw4MEiF2GbO3culy5dKrR98+bNgPvy8jyjR4/GZrPxxhtvFHm7tfPnzzNv3rz8n318fHjmmWcwTZOJEydy9uzZAscnJyeze/fu/J+HDx+OzWbj888/Z9++ffnbs7Oz+eMf/0hGRgaPPPJIkesP3IohQ4YAMGXKlALz7+Pj45kxY0aRv3Mr71OevXv3Av93kkJERORW6bouERERDzJp0iROnz7NqlWr+P7772nWrBlBQUFcunSJuLg4zp8/z4gRI+jevXuxnq979+4sXbqUPXv2FLgVHMC0adP485//TNOmTalbty6maXLkyBFOnjyJw+Fg1KhR+cd27NiRiRMnMnnyZIYPH06TJk2oV68eWVlZJCQkcPz4cXx9fRk+fHj+74wePZpDhw7x7bff0qdPHzp06IDD4SAhIYGDBw/Sr1+//ExhYWG88sorfPLJJwwdOpTw8HAcDgcxMTGcPXuWevXq8c4775T4/Y2MjGTdunWsW7eOvn370qVLF0zTZNu2bXTs2BHDMPIv6b+d9ylPdHR0/vsvIiJyO1TmRUREPEilSpX417/+xbJly1ixYgVHjhxhz549VKtWjdDQUEaMGEFERESxn+/JJ59k6dKlrFq1qlCZnzRpEps3b+bAgQNs2rQJcI8yP/vss/zmN78hKCiowPFPP/00bdq0Ye7cuezatYv169fj5+dHzZo1GTp0KH379i1wvLe3N1OnTmXZsmUsWbKEvXv35q8m36dPn/xR8jyjR4+madOmzJ07l3379pGZmUmdOnUYNWoUL7zwQpGLzN0qwzD45JNP+Oyzz1i8eDEbN24kKCiIp59+mpdffrnQv+F23qfMzEy+++47GjduTOvWrUucWURE7k2Gmbd0q4iIiNyTBg4cyLlz59i0adMNb6MmpWfVqlX89re/5d1332XYsGFWxxEREQ+lOfMiIiL3uPHjx3P58mUWLVpkdZRyzzRNZs+eTWhoKIMHD7Y6joiIeDCVeRERkXtc9+7d6dSpE7NnzyY7O9vqOOXad999x+HDhxk/fryughARkRLRZfYiIiIiIiIiHkYj8yIiIiIiIiIeRmVeRERERERExMOozIuIiIiIiIh4GJV5EREREREREQ+jMi8iIiIiIiLiYVTmRURERERERDyMyryIiIiIiIiIh1GZFxEREREREfEwKvMiIiIiIiIiHub/A5GYRhAjbDcaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "for i, planet in enumerate(df[\"Planet\"].cat.categories):\n",
    "    subset = df[df[\"Planet\"] == planet]\n",
    "    ax.plot(subset[\"Time\"], subset[\"Height_true\"], alpha=0.7, color=f\"C{i}\")\n",
    "    ax.scatter(subset[\"Time\"], subset[\"Height\"], alpha=0.7, label=planet, color=f\"C{i}\")\n",
    "\n",
    "ax.set(\n",
    "    xlabel=\"Time (seconds)\", ylabel=\"Height (meters)\", title=\"Trajectory Comparison\", ylim=(0, None)\n",
    ")\n",
    "ax.legend(title=\"Planet\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now aim to model this data. We again use the folowing equation (calling displacement $h$ for height):\n",
    "\n",
    "$$\n",
    "h = \\frac{1}{2} g_{p} t^2 + v_{0} t\n",
    "$$\n",
    "\n",
    "where $g_p$ now has a subscript to indicate the planet that we're throwing from.\n",
    "\n",
    "In Bambi, we'll do the following:\n",
    "\n",
    "`Height ~ I(Time**2):Planet + Time + 0`\n",
    "\n",
    "which corresponds one-to-one with the above formula. The intercept is eliminated since we start from $x=0$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR (pytensor.graph.rewriting.basic): Rewrite failure due to: random_make_inplace\n",
      "ERROR (pytensor.graph.rewriting.basic): node: t_rv{\"(),(),()->()\"}(*0-<RandomGeneratorType>, *1-<NoneTypeT>, *2-<Scalar(float64, shape=())>, 0.0, *3-<Scalar(float64, shape=())>)\n",
      "ERROR (pytensor.graph.rewriting.basic): TRACEBACK:\n",
      "ERROR (pytensor.graph.rewriting.basic): Traceback (most recent call last):\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/graph/rewriting/basic.py\", line 1933, in process_node\n",
      "    replacements = node_rewriter.transform(fgraph, node)\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/graph/rewriting/basic.py\", line 1086, in transform\n",
      "    return self.fn(fgraph, node)\n",
      "           ~~~~~~~^^^^^^^^^^^^^^\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/tensor/random/rewriting/basic.py\", line 50, in random_make_inplace\n",
      "    new_outputs = new_op.make_node(*node.inputs).outputs\n",
      "                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/tensor/random/op.py\", line 368, in make_node\n",
      "    size = normalize_size_param(size)\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/tensor/random/utils.py\", line 190, in normalize_size_param\n",
      "    shape = cast(as_tensor_variable(shape, ndim=1, dtype=\"int64\"), \"int64\")\n",
      "                 ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/tensor/__init__.py\", line 50, in as_tensor_variable\n",
      "    return _as_tensor_variable(x, name, ndim, **kwargs)\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/functools.py\", line 934, in wrapper\n",
      "    return dispatch(args[0].__class__)(*args, **kw)\n",
      "           ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/tensor/basic.py\", line 113, in _as_tensor_Variable\n",
      "    raise TypeError(\n",
      "        f\"Tensor type field must be a TensorType; found {type(x.type)}.\"\n",
      "    )\n",
      "TypeError: Tensor type field must be a TensorType; found <class 'pytensor.tensor.type_other.NoneTypeT'>.\n",
      "\n",
      "ERROR (pytensor.graph.rewriting.basic): Rewrite failure due to: random_make_inplace\n",
      "ERROR (pytensor.graph.rewriting.basic): node: t_rv{\"(),(),()->()\"}(*0-<RandomGeneratorType>, *1-<NoneTypeT>, *2-<Scalar(float64, shape=())>, 0.0, *3-<Scalar(float64, shape=())>)\n",
      "ERROR (pytensor.graph.rewriting.basic): TRACEBACK:\n",
      "ERROR (pytensor.graph.rewriting.basic): Traceback (most recent call last):\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/graph/rewriting/basic.py\", line 1933, in process_node\n",
      "    replacements = node_rewriter.transform(fgraph, node)\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/graph/rewriting/basic.py\", line 1086, in transform\n",
      "    return self.fn(fgraph, node)\n",
      "           ~~~~~~~^^^^^^^^^^^^^^\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/tensor/random/rewriting/basic.py\", line 50, in random_make_inplace\n",
      "    new_outputs = new_op.make_node(*node.inputs).outputs\n",
      "                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/tensor/random/op.py\", line 368, in make_node\n",
      "    size = normalize_size_param(size)\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/tensor/random/utils.py\", line 190, in normalize_size_param\n",
      "    shape = cast(as_tensor_variable(shape, ndim=1, dtype=\"int64\"), \"int64\")\n",
      "                 ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/tensor/__init__.py\", line 50, in as_tensor_variable\n",
      "    return _as_tensor_variable(x, name, ndim, **kwargs)\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/functools.py\", line 934, in wrapper\n",
      "    return dispatch(args[0].__class__)(*args, **kw)\n",
      "           ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^\n",
      "  File \"/home/tomas/oss/bambinos/bambi/.pixi/envs/dev/lib/python3.13/site-packages/pytensor/tensor/basic.py\", line 113, in _as_tensor_Variable\n",
      "    raise TypeError(\n",
      "        f\"Tensor type field must be a TensorType; found {type(x.type)}.\"\n",
      "    )\n",
      "TypeError: Tensor type field must be a TensorType; found <class 'pytensor.tensor.type_other.NoneTypeT'>.\n",
      "\n"
     ]
    },
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet_model = bmb.Model(\"Height ~ I(Time**2):Planet + Time + 0\", df)\n",
    "planet_model.build()\n",
    "planet_model.graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, I(Time ** 2):Planet, Time]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb63c312519e443b921d24a22e5711ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n"
     ]
    }
   ],
   "source": [
    "planet_fit = planet_model.fit(chains=4, idata_kwargs={\"log_likelihood\": True}, random_seed=SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has fit. Let's look at how we did recovering the simulated parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>1.759</td>\n",
       "      <td>0.147</td>\n",
       "      <td>1.498</td>\n",
       "      <td>2.044</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>2054.0</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(Time ** 2):Planet[Earth]</th>\n",
       "      <td>-4.998</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-5.145</td>\n",
       "      <td>-4.865</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>I(Time ** 2):Planet[Mars]</th>\n",
       "      <td>-1.884</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-1.925</td>\n",
       "      <td>-1.844</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>I(Time ** 2):Planet[PlanetX]</th>\n",
       "      <td>-3.017</td>\n",
       "      <td>0.036</td>\n",
       "      <td>-3.087</td>\n",
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       "      <td>1729.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <td>20.128</td>\n",
       "      <td>0.166</td>\n",
       "      <td>19.827</td>\n",
       "      <td>20.449</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1393.0</td>\n",
       "      <td>1714.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                mean     sd  hdi_3%  hdi_97%  mcse_mean  \\\n",
       "sigma                          1.759  0.147   1.498    2.044      0.003   \n",
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       "I(Time ** 2):Planet[Mars]     -1.884  0.022  -1.925   -1.844      0.001   \n",
       "I(Time ** 2):Planet[PlanetX]  -3.017  0.036  -3.087   -2.953      0.001   \n",
       "Time                          20.128  0.166  19.827   20.449      0.004   \n",
       "\n",
       "                              mcse_sd  ess_bulk  ess_tail  r_hat  \n",
       "sigma                           0.003    2054.0    1938.0    1.0  \n",
       "I(Time ** 2):Planet[Earth]      0.001    1833.0    2431.0    1.0  \n",
       "I(Time ** 2):Planet[Mars]       0.000    1428.0    1763.0    1.0  \n",
       "I(Time ** 2):Planet[PlanetX]    0.001    1519.0    1729.0    1.0  \n",
       "Time                            0.003    1393.0    1714.0    1.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(planet_fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting the gravities back to the physical value,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g for Earth: -10.29 to -9.71 meters (True: -9.81 m)\n",
      "g for Mars: -3.85 to -3.68 meters (True: -3.72 m)\n",
      "g for PlanetX: -6.18 to -5.90 meters (True: -6.0 m)\n",
      "Initial velocity: 19.80 to 20.45 meters per second (True: 20 m/s)\n"
     ]
    }
   ],
   "source": [
    "hdi = az.hdi(planet_fit.posterior, hdi_prob=0.95)\n",
    "print(f\"g for Earth: {2*hdi['I(Time ** 2):Planet'].sel({'I(Time ** 2):Planet_dim':'Earth', 'hdi':'lower'}):.2f} to {2*hdi['I(Time ** 2):Planet'].sel({'I(Time ** 2):Planet_dim':'Earth', 'hdi':'higher'}):.2f} meters (True: -9.81 m)\")\n",
    "print(f\"g for Mars: {2*hdi['I(Time ** 2):Planet'].sel({'I(Time ** 2):Planet_dim':'Mars', 'hdi':'lower'}):.2f} to {2*hdi['I(Time ** 2):Planet'].sel({'I(Time ** 2):Planet_dim':'Mars', 'hdi':'higher'}):.2f} meters (True: -3.72 m)\")\n",
    "print(f\"g for PlanetX: {2*hdi['I(Time ** 2):Planet'].sel({'I(Time ** 2):Planet_dim':'PlanetX', 'hdi':'lower'}):.2f} to {2*hdi['I(Time ** 2):Planet'].sel({'I(Time ** 2):Planet_dim':'PlanetX', 'hdi':'higher'}):.2f} meters (True: -6.0 m)\")\n",
    "print(f\"Initial velocity: {hdi['Time'].sel(hdi='lower'):.2f} to {hdi['Time'].sel(hdi='higher'):.2f} meters per second (True: 20 m/s)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we're pretty close to recovering most the parameters, but the fit isn't great. Plotting the posteriors for $g$ against the true values,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "earth_posterior = -2 * planet_fit.posterior[\"I(Time ** 2):Planet\"].sel({\"I(Time ** 2):Planet_dim\": \"Earth\"})\n",
    "planetx_posterior = -2 * planet_fit.posterior[\"I(Time ** 2):Planet\"].sel({\"I(Time ** 2):Planet_dim\": \"PlanetX\"})\n",
    "mars_posterior = -2 * planet_fit.posterior[\"I(Time ** 2):Planet\"].sel({\"I(Time ** 2):Planet_dim\": \"Mars\"}) \n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12, 6))\n",
    "az.plot_posterior(earth_posterior, ref_val=9.81, ax=axs[0])\n",
    "axs[0].set_title(\"Posterior $g$ on Earth\")\n",
    "az.plot_posterior(mars_posterior, ref_val=3.72, ax=axs[1])\n",
    "axs[1].set_title(\"Posterior $g$ on Mars\")\n",
    "az.plot_posterior(planetx_posterior, ref_val=6.0, ax=axs[2])\n",
    "axs[2].set_title(\"Posterior $g$ on PlanetX\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit seems to work, more or less, but certainly could be improved.\n",
    "\n",
    "### Adding a prior\n",
    "\n",
    "But, we can do better! We have a [very good idea of the acceleration due to gravity on Earth](https://en.wikipedia.org/wiki/Gravity_of_Earth) and [Mars](https://en.wikipedia.org/wiki/Gravity_of_Mars), so why not use that information? From an experimental standpoint, we can consider these throws from a calibration mindset, allowing us to get some information on the resolution of our detector, and our throwing apparatus. The model will spend considerably less time trying to pin down those parameters, and will better explore other parameters with already good values of the $g$ terms locked in.\n",
    "\n",
    "For Earth, at the extremes, $g$ takes values as low as 9.78 $m$/$s^2$ (at the Equator) up to 9.83 (at the Poles). So we can add a very strong prior,\n",
    "\n",
    "$$\n",
    "g_{\\text{Earth}} \\sim \\text{Normal}(-9.81, 0.025)\n",
    "$$\n",
    "\n",
    "For Mars, we know the mean value is about 3.72 $m$/$s^2$. There's less information on local variation readily available by a cursory search, _however_ we know that the radius of Mars is about half that of Earth, so $\\sigma = \\frac{0.025}{2} = 0.0125$ might make sense, but to be conservative we'll round that up to $\\sigma = 0.02$.\n",
    "\n",
    "$$\n",
    "g_{\\text{Mars}} \\sim \\text{Normal}(-3.72, 0.02)\n",
    "$$\n",
    "\n",
    "For PlanetX, we must use a very loose prior. We might say that we know the ball took longer to fall than Earth, but not as long as on Mars, so we can split the difference. Then set a very wide $\\sigma$ value.\n",
    "\n",
    "$$\n",
    "g_{\\text{PlanetX}} \\sim \\text{Normal}(\\frac{-9.81 - 3.72}{2}, 3) = \\text{Normal}(-6.77, 3)\n",
    "$$\n",
    "\n",
    "Since these correspond to $g/2$, we'll divide all values by 2 when putting them into Bambi. Additionally, we know the balls landed eventually, so $g$ _must be_ negative. We'll truncate the upper limit of the distribution at 0.\n",
    "\n",
    "Now, for defining this in Bambi, the term of interest is `I(Time ** 2):Planet`. Often, you set one prior that applies to all groups, however, if you want to set each group individually, you can pass a list to the `bmb.Prior` definition. [The broadcasting rules from PyMC apply here](https://github.com/bambinos/bambi/issues/778), so it could equivalently take a numpy array. You'll notice that the priors are passed alphabetically by group name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [Height, I(Time ** 2):Planet, Time, sigma]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>14.466</td>\n",
       "      <td>13.809</td>\n",
       "      <td>0.025</td>\n",
       "      <td>36.595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(Time ** 2):Planet[Earth]</th>\n",
       "      <td>-4.905</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-4.928</td>\n",
       "      <td>-4.883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(Time ** 2):Planet[Mars]</th>\n",
       "      <td>-1.860</td>\n",
       "      <td>0.010</td>\n",
       "      <td>-1.880</td>\n",
       "      <td>-1.841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(Time ** 2):Planet[PlanetX]</th>\n",
       "      <td>-3.622</td>\n",
       "      <td>1.509</td>\n",
       "      <td>-6.360</td>\n",
       "      <td>-0.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <td>0.520</td>\n",
       "      <td>14.788</td>\n",
       "      <td>-26.565</td>\n",
       "      <td>27.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[76]</th>\n",
       "      <td>-106.703</td>\n",
       "      <td>93.046</td>\n",
       "      <td>-269.417</td>\n",
       "      <td>66.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[77]</th>\n",
       "      <td>-116.760</td>\n",
       "      <td>98.348</td>\n",
       "      <td>-290.948</td>\n",
       "      <td>61.963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[78]</th>\n",
       "      <td>-127.270</td>\n",
       "      <td>103.780</td>\n",
       "      <td>-314.577</td>\n",
       "      <td>58.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[79]</th>\n",
       "      <td>-138.232</td>\n",
       "      <td>109.346</td>\n",
       "      <td>-338.301</td>\n",
       "      <td>53.774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[80]</th>\n",
       "      <td>-149.647</td>\n",
       "      <td>115.049</td>\n",
       "      <td>-358.999</td>\n",
       "      <td>55.671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 mean       sd   hdi_3%  hdi_97%\n",
       "sigma                          14.466   13.809    0.025   36.595\n",
       "I(Time ** 2):Planet[Earth]     -4.905    0.012   -4.928   -4.883\n",
       "I(Time ** 2):Planet[Mars]      -1.860    0.010   -1.880   -1.841\n",
       "I(Time ** 2):Planet[PlanetX]   -3.622    1.509   -6.360   -0.915\n",
       "Time                            0.520   14.788  -26.565   27.992\n",
       "...                               ...      ...      ...      ...\n",
       "mu[76]                       -106.703   93.046 -269.417   66.054\n",
       "mu[77]                       -116.760   98.348 -290.948   61.963\n",
       "mu[78]                       -127.270  103.780 -314.577   58.189\n",
       "mu[79]                       -138.232  109.346 -338.301   53.774\n",
       "mu[80]                       -149.647  115.049 -358.999   55.671\n",
       "\n",
       "[86 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "priors = {\n",
    "    \"I(Time ** 2):Planet\": bmb.Prior(\n",
    "        \"TruncatedNormal\",\n",
    "        mu=[\n",
    "            -9.81/2,  # Earth\n",
    "            -3.72/2,  # Mars\n",
    "            -6.77/2    # PlanetX\n",
    "        ],\n",
    "        sigma=[ \n",
    "            0.025/2,  # Earth \n",
    "            0.02/2,   # Mars\n",
    "            3/2       # PlanetX\n",
    "        ],\n",
    "        upper=[0, 0, 0]\n",
    "    )} \n",
    "\n",
    "planet_model_with_prior = bmb.Model(\n",
    "    'Height ~ I(Time**2):Planet + Time + 0',\n",
    "    df,\n",
    "    priors=priors\n",
    ")\n",
    "\n",
    "planet_model_with_prior.build()\n",
    "idata = planet_model_with_prior.prior_predictive()\n",
    "az.summary(idata.prior, kind=\"stats\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we've sampled the prior predictive and can see that our priors are correctly specified to the associated planets.\n",
    "\n",
    "Next we fit the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, I(Time ** 2):Planet, Time]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e89a9496529c4c00972205bbe3fb23f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n"
     ]
    }
   ],
   "source": [
    "planet_fit_with_prior = planet_model_with_prior.fit(chains=4, idata_kwargs={\"log_likelihood\": True}, random_seed=SEED)\n",
    "az.summary(planet_fit_with_prior)\n",
    "planet_model_with_prior.predict(planet_fit_with_prior, kind=\"pps\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>1.759</td>\n",
       "      <td>0.142</td>\n",
       "      <td>1.495</td>\n",
       "      <td>2.024</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>3333.0</td>\n",
       "      <td>2373.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(Time ** 2):Planet[Earth]</th>\n",
       "      <td>-4.907</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-4.929</td>\n",
       "      <td>-4.884</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(Time ** 2):Planet[Mars]</th>\n",
       "      <td>-1.862</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-1.879</td>\n",
       "      <td>-1.847</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2054.0</td>\n",
       "      <td>2614.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(Time ** 2):Planet[PlanetX]</th>\n",
       "      <td>-2.985</td>\n",
       "      <td>0.023</td>\n",
       "      <td>-3.025</td>\n",
       "      <td>-2.940</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2282.0</td>\n",
       "      <td>2772.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <td>19.960</td>\n",
       "      <td>0.075</td>\n",
       "      <td>19.827</td>\n",
       "      <td>20.103</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2025.0</td>\n",
       "      <td>2249.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                mean     sd  hdi_3%  hdi_97%  mcse_mean  \\\n",
       "sigma                          1.759  0.142   1.495    2.024      0.002   \n",
       "I(Time ** 2):Planet[Earth]    -4.907  0.012  -4.929   -4.884      0.000   \n",
       "I(Time ** 2):Planet[Mars]     -1.862  0.009  -1.879   -1.847      0.000   \n",
       "I(Time ** 2):Planet[PlanetX]  -2.985  0.023  -3.025   -2.940      0.000   \n",
       "Time                          19.960  0.075  19.827   20.103      0.002   \n",
       "\n",
       "                              mcse_sd  ess_bulk  ess_tail  r_hat  \n",
       "sigma                           0.002    3333.0    2373.0    1.0  \n",
       "I(Time ** 2):Planet[Earth]      0.000    4360.0    2943.0    1.0  \n",
       "I(Time ** 2):Planet[Mars]       0.000    2054.0    2614.0    1.0  \n",
       "I(Time ** 2):Planet[PlanetX]    0.000    2282.0    2772.0    1.0  \n",
       "Time                            0.001    2025.0    2249.0    1.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(planet_fit_with_prior)[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see some improvements here! Off the cuff, these look better, you'll notice the $v_0$ coefficient on `Time` covers the true value of 20 m/s.\n",
    "\n",
    "Now taking a look at the effects before and after adding the prior on the gravities,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "earth_posterior_2 = -2 *  planet_fit_with_prior.posterior[\"I(Time ** 2):Planet\"].sel({\"I(Time ** 2):Planet_dim\": \"Earth\"})\n",
    "mars_posterior_2 = -2 * planet_fit_with_prior.posterior[\"I(Time ** 2):Planet\"].sel({\"I(Time ** 2):Planet_dim\": \"Mars\"})\n",
    "planetx_posterior_2 = -2 * planet_fit_with_prior.posterior[\"I(Time ** 2):Planet\"].sel({\"I(Time ** 2):Planet_dim\": \"PlanetX\"})\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, figsize=(12, 6), sharex='col')\n",
    "az.plot_posterior(earth_posterior, ref_val=9.81, ax=axs[0,0])\n",
    "axs[0,0].set_title(\"Earth $g$ - No Prior\")\n",
    "az.plot_posterior(mars_posterior, ref_val=3.72, ax=axs[0,1])\n",
    "axs[0,1].set_title(\"Mars $g$ - No Prior\")\n",
    "az.plot_posterior(planetx_posterior, ref_val=6.0, ax=axs[0,2])\n",
    "axs[0,2].set_title(\"PlanetX $g$ - No Prior\")\n",
    "\n",
    "az.plot_posterior(earth_posterior_2, ref_val=9.81, ax=axs[1,0])\n",
    "axs[1,0].set_title(\"Earth $g$ - Priors Used\")\n",
    "az.plot_posterior(mars_posterior_2, ref_val=3.72, ax=axs[1,1])\n",
    "axs[1,1].set_title(\"Mars $g$ - Priors Used\")\n",
    "az.plot_posterior(planetx_posterior_2, ref_val=6.0, ax=axs[1,2])\n",
    "axs[1,2].set_title(\"PlanetX $g$ - Priors Used\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the prior gives smaller uncertainties for Earth and Mars by design, however, we can see the estimate for PlanetX has also considerably improved by injecting our knowledge into the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sun Sep 28 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.13.7\n",
      "IPython version      : 9.4.0\n",
      "\n",
      "bambi     : 0.14.1.dev58+gb25742785.d20250928\n",
      "numpy     : 2.3.3\n",
      "arviz     : 0.22.0\n",
      "pandas    : 2.3.2\n",
      "matplotlib: 3.10.6\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dev",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
